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dataset_file.json
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[
{
"figure_path": "neurips_figures/2310.07235/RelGradNormsByLayerAllInits.png",
"caption": "Relative gradient norms of feature (left axis, solid) and of attention (right axis, stylized) parameters for $l\\in[1,5,10]$ and $L=10$, sampled every $25$ epochs. Test accuracy is at the top. Both attention and feature gradients at the first, middle, and last layer of the network with both balanced initializations are much larger than with unbalanced initialization (note axis scales).",
"source": "neurips/2310.07235/theory.tex",
"arxiv_id": "neurips/2310.07235",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.07235/RelativechangeInParams.png",
"caption": "Frac. of sig. params with relative change $>0.5$",
"source": "neurips/2310.07235/theory.tex",
"arxiv_id": "neurips/2310.07235",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.07235/RelativeChangeInParams5Layer.png",
"caption": "Frac. of sig. params with relative change $>0.05$",
"source": "neurips/2310.07235/theory.tex",
"arxiv_id": "neurips/2310.07235",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.07235/FracZeroChange_SGD5Layer_v2.png",
"caption": "$L=5$",
"source": "neurips/2310.07235/appendix.tex",
"arxiv_id": "neurips/2310.07235",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.07234/Empirical_Analysis_v2.jpg",
"caption": "Empirical study of prompt-based continual learning under different pre-training paradigms.",
"source": "neurips/2310.07234/Camera_ready.tex",
"arxiv_id": "neurips/2310.07234",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.07123/beta-left_speed.png",
"caption": "Correlation between the beta power and HF provided by patients from all cinical sessions.",
"source": "neurips/2310.07123/main.tex",
"arxiv_id": "neurips/2310.07123",
"type": "Scatter Plot"
},
{
"figure_path": "neurips_figures/2310.04655/ablation_supp_vr.png",
"caption": "ViLT-VR.",
"source": "neurips/2310.04655/neurips_2023.tex",
"arxiv_id": "neurips/2310.04655",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.03024/mask_fill_4.png",
"caption": "Example of the performance of the mask filling model.",
"source": "neurips/2310.03024/astroclip-arxiv-iclrstyle.tex",
"arxiv_id": "neurips/2310.03024",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.03024/mask_fill_3.png",
"caption": "Example of the performance of the mask filling model.",
"source": "neurips/2310.03024/astroclip-arxiv-iclrstyle.tex",
"arxiv_id": "neurips/2310.03024",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.03024/mask_fill_2.png",
"caption": "Example of the performance of the mask filling model.",
"source": "neurips/2310.03024/astroclip-arxiv-iclrstyle.tex",
"arxiv_id": "neurips/2310.03024",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.03024/attention_1.png",
"caption": "Examples of attention maps of the cross-attention layer of the spectrum encoder.",
"source": "neurips/2310.03024/astroclip-arxiv-iclrstyle.tex",
"arxiv_id": "neurips/2310.03024",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2310.02230/color_diff_vs_ood_metric_comparison.png",
"caption": "ColorDSprites",
"source": "neurips/2310.02230/main.tex",
"arxiv_id": "neurips/2310.02230",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.01835/rel_test_10.png",
"caption": "Top 10.",
"source": "neurips/2310.01835/neurips_data_2023.tex",
"arxiv_id": "neurips/2310.01835",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.01835/lab_hom_10.png",
"caption": "Top 10.",
"source": "neurips/2310.01835/neurips_data_2023.tex",
"arxiv_id": "neurips/2310.01835",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.01835/vt_detections.png",
"caption": "Distribution of VirusTotal detections per label.",
"source": "neurips/2310.01835/neurips_data_2023.tex",
"arxiv_id": "neurips/2310.01835",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.01835/labels.png",
"caption": "Distribution of sample count per labels with respect to the data subset.",
"source": "neurips/2310.01835/neurips_data_2023.tex",
"arxiv_id": "neurips/2310.01835",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.01455/s128b40_metrics.png",
"caption": "Log-log plot showing the resulting metrics of design points sampled in the Sobol stage (orange dots) and the Bayesian optimisation stage (purple squares) where the darker the colour of a Bayesian point, the later the iteration that design point corresponds to. Stars indicate the non-dominated points--the Pareto optimal design points.",
"source": "neurips/2310.01455/main.tex",
"arxiv_id": "neurips/2310.01455",
"type": "Scatter Plot"
},
{
"figure_path": "neurips_figures/2310.00675/res_all_KF.png",
"caption": "",
"source": "neurips/2310.00675/main.tex",
"arxiv_id": "neurips/2310.00675",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2310.00675/tars_toy.png",
"caption": "Toy",
"source": "neurips/2310.00675/main.tex",
"arxiv_id": "neurips/2310.00675",
"type": "Others"
},
{
"figure_path": "neurips_figures/2309.16342/2D_DAM_100.png",
"caption": "Our results.",
"source": "neurips/2309.16342/neurips_data_2023.tex",
"arxiv_id": "neurips/2309.16342",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.16342/2D_PF_60.png",
"caption": "Comparison of SPH ($\\circ$) and series solutions ($-$) for Poiseuille flow at $Re=0.0125$.",
"source": "neurips/2309.16342/neurips_data_2023.tex",
"arxiv_id": "neurips/2309.16342",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.16342/scaling.png",
"caption": "Scaling evaluation on all datasets. The $x$-axis shows the amount of available data, and the $y$-axis shows the position MSE loss values. The model is GNS-10-64 trained for 1M steps (with 40k steps early stopping). Every mark represents a new GNS instance trained with a different amount of data.",
"source": "neurips/2309.16342/neurips_data_2023.tex",
"arxiv_id": "neurips/2309.16342",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.16115/figures_composition2_indep_param_beta32_base_gflownets_32.png",
"caption": "Base at \\(\\beta=32\\)",
"source": "neurips/2309.16115/main.tex",
"arxiv_id": "neurips/2309.16115",
"type": "Others"
},
{
"figure_path": "neurips_figures/2309.15809/ADNI_AV1.png",
"caption": "Visualization of the canonical correlation results of ADNI for the total five projection dimensions ($r$). All the methods are applied to both the entire dataset and individual subgroups. The closer each subgroup's curve is to the overall curve, the better.",
"source": "neurips/2309.15809/fair_cca/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/X_CCA.png",
"caption": "Scatter plot of the synthetic data points after projected to the 2-dimensional space. The distributions of the two groups after projection by CCA are orthogonal to each other. Our SF-CCA and MF-CCA can make the distributions of the two groups close to each other.",
"source": "neurips/2309.15809/sec_exp.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Scatter Plot"
},
{
"figure_path": "neurips_figures/2309.15809/runtime_K.png",
"caption": "Computation time (mean$\\pm$std) of 10 repeated experiments for the total seven projection dimensions on synthetic data comprising varying numbers of subgroups ($K$). The number of features is fixed at $d=100$.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/runtime_fixP.png",
"caption": "Computation time (mean$\\pm$std) of 10 repeated experiments for the total three projection dimensions on synthetic data comprising four subgroups ($K$). The number of features is fixed at $d=100$, and the number of groups is held constant at $K=5$, while the number of samples varies.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/runtime_fixN.png",
"caption": "Computation time (mean$\\pm$std) of 10 repeated experiments for the total three projection dimensions on synthetic data comprising four subgroups ($K$). The number of samples is fixed at $N=2000$, while the number of features varies.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/lambda_sensitivity_SYNTHE.png",
"caption": "Sensitivity of correlation and disparity error to $\\lambda$ in SF-CCA framework. Higher $\\lambda$ emphasizes fairness over correlation (accuracy). Moving right to left, accuracy drops as fairness improves (smaller disparity). A notable trend links higher correlation with reduced fairness.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/UCLA1.png",
"caption": "Visualization of the canonical correlation results of MHAAPS (Sex) for the total two projection dimensions ($r$). All the methods are applied to both the entire dataset and individual subgroups. The closer each subgroup's curve is to the overall curve, the better.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/NHA_EDU1.png",
"caption": "Visualization of the canonical correlation results of NHANES (Education \\& Race) for the total five projection dimensions ($r$). All the methods are applied to both the entire dataset and individual subgroups. The closer each subgroup's curve is to the overall curve, the better.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/Synthetic_Data1.png",
"caption": "Visualization of the canonical correlation results on synthetic data for the total five projection dimensions ($r$). All the methods are applied to both the entire dataset and individual subgroups. The closer each subgroup's curve is to the overall curve, the better.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/disparity_K.png",
"caption": "Aggregate disparity of 1st projection dimension on synthetic data comprising varying numbers of subgroups ($K$).",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15809/Bar_Syn.png",
"caption": "Group distributions of the studied datasets.",
"source": "neurips/2309.15809/sec_add_b.tex",
"arxiv_id": "neurips/2309.15809",
"type": "Bar Chart"
},
{
"figure_path": "neurips_figures/2309.15286/Dataset_GENES_pointsetsize.jpg",
"caption": "Local Optimality ($1+\\eps$) against Number of Points in the Base Set for $k = 5,10,15,20$.",
"source": "neurips/2309.15286/main.tex",
"arxiv_id": "neurips/2309.15286",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.15286/Dataset_GENESExp1_3000_5iters.jpg",
"caption": "Local Optimality ($1+\\eps$) against $k$ for GENES and MNIST datasets, and random datasets of the same dimension. Each stream had $10$ point sets of size $3000$, with $k$ ranging from $1$ to $20$.",
"source": "neurips/2309.15286/main.tex",
"arxiv_id": "neurips/2309.15286",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/ml10_10latent_longer_indiv.png",
"caption": "Our ML10 Results",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/combined_obj.png",
"caption": "Tuning Combined Weighting",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/ti_traintime.png",
"caption": "Grid-World LR",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/ti_grid.png",
"caption": "Walker",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/analysis_return.png",
"caption": "Walker Latent Gradient Norm",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/walker_rnn.png",
"caption": "Cheetah-Vel",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/grid_rnn.png",
"caption": "Grid Show",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/mc-ls-precollect.png",
"caption": "RNN+HN outperforms VI+HN on MC-LS (MineCraft) environment.",
"source": "neurips/2309.14970/neurips_2023.tex",
"arxiv_id": "neurips/2309.14970",
"type": "Line Chart"
},
{
"figure_path": "neurips_figures/2309.14970/walker.png",
"caption": "Cheetah-Vel",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.14970/grid.png",
"caption": "Grid Show",
"source": "neurips/2309.14970/neurips_2023.tex",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.14970/walker_hyper_rnn.png",
"caption": "Cheetah-Dir",
"source": "neurips/2309.14970/neurips_2023.tex",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.14502/FNAL_uq_result.png",
"caption": "The results from DGPA surrogate model of the FNAL Booster Accelerator. (a) Shows the predictions on the in-distribution and OOD samples along with the associated uncertainty values. The middle region with the high frequency component on the time series represents OOD samples while the initial and tail-end regions represent in-distribution data samples. (b) Shows the predictions and uncertainty values for the synthetic case where the data is intentionally made to enter an OOD region.",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.14502/Siamese_Results_v4.png",
"caption": "The results on errant beam predictions from SNGP-SNN model. (a) ROC curves with the bands created by smearing the predictions with associated uncertainty values. (b) The scatter plot representing classifier output vs uncertainty values.",
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"type": "Scatter Plot"
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"figure_path": "neurips_figures/2309.14062/curves.png",
"caption": "Singular values",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13896/holder-space.png",
"caption": "Comparison of convergence under different $\\phi$ functions (e.g., linear and those in Holder space).",
"source": "neurips/2309.13896/sec-appendix.tex",
"arxiv_id": "neurips/2309.13896",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13786/g_female_black_n_100_m_cvar.png",
"caption": "Learning tighter bounds on functionals of interest for protected groups. On the left, a bound is optimized for CVaR with $\\beta=0.75$, and on the right a bound is optimized for the VaR Interval $[0.5, 0.9]$. In both cases the optimized bounds are tightest on both the target metric as well as the mean, illustrating the power of adaptation both to particular quantile ranges as well as real loss distributions.",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13786/smooth_delta.png",
"caption": "Plot of smoothed median function with $\\beta=0.5$ and $a=0.01$",
"source": "neurips/2309.13786/appendix_exp.tex",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13786/ml-1m_recall_atkinson_1.0_t_vs_b.png",
"caption": "We select two hypotheses $h_0$ and $h_1$ with different bounds on Atkinson index produced using 2000 validation samples, and once again visualize the Lorenz curves induced by each. Tighter control on the Atkinson index leads to a more equal distribution of the loss (especially across the middle of the distribution, which aligns with the choice of $\\epsilon$), highlighting the utility of being able to target such a metric in conservative model selection.",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13786/rxrx1_balanced_acc_expected_gini_2500_val.png",
"caption": "Left: Bounds on the expected loss, scaled Gini coefficient, and total objective across different hypotheses. Right: Lorenz curves induced by choosing a hypothesis based on the expected loss bound versus the bound on the total objective. The y-axis shows the cumulative share of the loss that is incurred by the best-off $\\beta$ proportion of the population, where a perfectly fair predictor would produce a distribution along the line $y=x$.",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13378/app_loss.png",
"caption": "Training losses and evaluation losses on AIR-BJ. w: with. w/o: without.",
"source": "neurips/2309.13378/main_crv.tex",
"arxiv_id": "neurips/2309.13378",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.13377/label_imbalance_camelyon_isic.png",
"caption": "Number of datapoints separated by class for Camelyon-17 and ISIC datasets. There is significant label imbalance for the ISIC dataset.",
"source": "neurips/2309.13377/main.tex",
"arxiv_id": "neurips/2309.13377",
"type": "Bar Chart"
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"figure_path": "neurips_figures/2309.11702/exp_2_regret.png",
"caption": "Ablation study on heuristic search (w.r.t $D^p_\\star \\in [1, 10, 100]$).",
"source": "neurips/2309.11702/experiments.tex",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.11702/exp_1_syn_cost_1.png",
"caption": "Comparison between payment-free vs. payment-efficient incentive designs.",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2309.11702/ablation_movie_reward_plot.png",
"caption": "Ablation study on heuristic search (w.r.t $D^p_\\star \\in [1, 10, 100]$).",
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"type": "Scatter Plot"
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"figure_path": "neurips_figures/2309.11702/exp_1_movielens_cost_1.png",
"caption": "Comparison between payment-free vs. payment-efficient incentive designs.",
"source": "neurips/2309.11702/appendix.tex",
"arxiv_id": "neurips/2309.11702",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.11600/hyper_appendix.jpg",
"caption": "Extended Analysis on Hyperparameter Sensitivity.",
"source": "neurips/2309.11600/body.tex",
"arxiv_id": "neurips/2309.11600",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.07867/distance_plot.png",
"caption": "\\small Comparison of the statistical distances between the true and generated data distributions over the course of training. The blue, green, and orange curves are for ``Gauss ELBO,'' ``Beta ELBO,'' and ``Beta KLUB,'' respectively. From the left to right are the plots for Wasserstein-1 distance, Jensen--Shannon divergence, and Hellinger distance, respectively.",
"source": "neurips/2309.07867/beta_diffusion_v2.tex",
"arxiv_id": "neurips/2309.07867",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.06402/FigXINN-01.png",
"caption": "Invertible Neural Network readouts produce qualitatively similar results to Flow readout models. Data shown is the same as Fig. 2C, except overlaid with INN readout model (purple)",
"source": "neurips/2309.06402/supp.tex",
"arxiv_id": "neurips/2309.06402",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.06402/SupFigXDynAccuracy-Arneodo-Unwarped_Linear-01.png",
"caption": "Linear-NODE trained on synthetic neural activity from linearly-embedded Arneodo system",
"source": "neurips/2309.06402/supp.tex",
"arxiv_id": "neurips/2309.06402",
"type": "Line Chart"
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"figure_path": "neurips_figures/2309.06402/InjectivityBarPlot-01.png",
"caption": "Injectivity of the Flow readout across state dimensionalities. Each bar indicates the mean value of 5 randomly initialized ODIN models for each state dimensionality. Results from individual models are plotted as points.",
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"arxiv_id": "neurips/2309.06402",
"type": "Bar Chart"
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"figure_path": "neurips_figures/2309.06402/FigXHyperparams-01.png",
"caption": "Example hyperparameter sweeps for ODIN and MLP-NODE",
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"type": "Scatter Plot"
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"figure_path": "neurips_figures/2308.14364/RL_ENHANCE.png",
"caption": "Comparison of the results of enhancements to PPO.",
"source": "neurips/2308.14364/main.tex",
"arxiv_id": "neurips/2308.14364",
"type": "Bar Chart"
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{
"figure_path": "neurips_figures/2308.14364/RL_ALGS.png",
"caption": "Comparison of various RL algorithms.",
"source": "neurips/2308.14364/main.tex",
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"figure_path": "neurips_figures/2308.12580/human_reasons_for_limitations.png",
"caption": "Reasons that served as limitations while evaluating the effort to reproduce.",
"source": "neurips/2308.12580/effortly.tex",
"arxiv_id": "neurips/2308.12580",
"type": "Bar Chart"
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{
"figure_path": "neurips_figures/2308.12580/human_reasons_for_difficulty.png",
"caption": "Reasons that made it difficult to reproduce.",
"source": "neurips/2308.12580/effortly.tex",
"arxiv_id": "neurips/2308.12580",
"type": "Bar Chart"
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{
"figure_path": "neurips_figures/2308.12580/human_reasons_for_easiness.png",
"caption": "Reasons that eased the effort to reproduce.",
"source": "neurips/2308.12580/effortly.tex",
"arxiv_id": "neurips/2308.12580",
"type": "Bar Chart"
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"caption": "",
"source": "neurips/2308.08778/experiments.tex",
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"type": "Line Chart"
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"figure_path": "neurips_figures/2308.08778/color_noises_1.png",
"caption": "Test color noise $e = 0.1$",
"source": "neurips/2308.08778/appendix.tex",
"arxiv_id": "neurips/2308.08778",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2308.04024/Pong.png",
"caption": "RL Loss Comparison Training Curves",
"source": "neurips/2308.04024/main.tex",
"arxiv_id": "neurips/2308.04024",
"type": "Scatter Plot"
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"figure_path": "neurips_figures/2307.13855/rohrer100k_pvals1.png",
"caption": "Learned values of $p$ in first SCS layer for Rohrer100K on CIFAR-10 ($32 \\times 32$, Initial Testing).",
"source": "neurips/2307.13855/main.tex",
"arxiv_id": "neurips/2307.13855",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.10810/2.png",
"caption": "Experimental results",
"source": "neurips/2307.10810/main.tex",
"arxiv_id": "neurips/2307.10810",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.10524/beta.png",
"caption": "Average awards with varying choices of the hyper-parameter $\\beta$ in the robustness budget of \\ouralg. Shadow area depicts the range of standard deviations for $5$ random tests. Left: $\\beta=1, 10,10^2,10^3$, and $\\infty$ (directly applying the MPC baseline); Right: $\\beta=0, 0.05, 0.5, 1$, and $\\infty$.",
"source": "neurips/2307.10524/main.tex",
"arxiv_id": "neurips/2307.10524",
"type": "Line Chart"
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"figure_path": "neurips_figures/2307.08863/ipd_ablation_8.png",
"caption": "Ablation experiment on the Iterated Prisoner's Dilemma. We show the effect of disabling exploration, using only $k$-step TD errors, using only one-step TD errors, disabling distributional RL, disabling target networks, using the $V$ formulation over the $U$ formulation, and training with a fixed $\\gamma=0.95$. For each configuration (row) we train 5 models for 500 outer loops. In the leftmost column, we show short-term TD error (over $k=10$ steps, as in training) and long-term TD error (over 100 steps, as a validation); the difference between these is due to bootstrapping. The horizontal axis measures number of outer loops performed. In the middle column, the returns $f(x)$ of agents that are being trained on the model (with $\\gamma=0.95$) and are reset every 10 outer loops. For those agents we continually test their exploitability; the third column shows their returns against agents trained to exploit them.",
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"arxiv_id": "neurips/2307.08863",
"type": "Line Chart"
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"figure_path": "neurips_figures/2307.07907/causal_mask_prob_WipeCausal_all.png",
"caption": "Estimated Causal Graphs of the Wipe task in Robosuite.",
"source": "neurips/2307.07907/camera_ready.tex",
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"type": null
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"figure_path": "neurips_figures/2307.07907/causal_mask_prob_DoorCausal_all.png",
"caption": "Estimated Causal Graphs of the Door task in Robosuite.",
"source": "neurips/2307.07907/camera_ready.tex",
"arxiv_id": "neurips/2307.07907",
"type": "Heat Map"
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"figure_path": "neurips_figures/2307.07907/causal_mask_prob_StackCausal_all.png",
"caption": "Estimated Causal Graphs of the Stack task in Robosuite.",
"source": "neurips/2307.07907/camera_ready.tex",
"arxiv_id": "neurips/2307.07907",
"type": "Heat Map"
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"figure_path": "neurips_figures/2307.07907/causal_mask_prob_LiftCausal_all.png",
"caption": "Estimated Causal Graphs of the Lift task in Robosuite.",
"source": "neurips/2307.07907/camera_ready.tex",
"arxiv_id": "neurips/2307.07907",
"type": null
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"figure_path": "neurips_figures/2307.07907/causal_graph.png",
"caption": "Estimated Causal Graphs of four tasks in Carla.",
"source": "neurips/2307.07907/camera_ready.tex",
"arxiv_id": "neurips/2307.07907",
"type": "Heat Map"
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{
"figure_path": "neurips_figures/2307.05916/loss_curve_sex.png",
"caption": "Validation AUROC per training epoch for sex classification",
"source": "neurips/2307.05916/NeurIPS_main.tex",
"arxiv_id": "neurips/2307.05916",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.05916/window_analysis_all.png",
"caption": "Inner-subject accuracy of sex classification.",
"source": "neurips/2307.05916/NeurIPS_main.tex",
"arxiv_id": "neurips/2307.05916",
"type": "Bar Chart"
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{
"figure_path": "neurips_figures/2307.04204/cifar_mean_acttanh_width256_label0.png",
"caption": "$m=512$",
"source": "neurips/2307.04204/A1_exp.tex",
"arxiv_id": "neurips/2307.04204",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.04204/cifar_mean_actelu_width64.png",
"caption": "$m=128$",
"source": "neurips/2307.04204/A1_exp.tex",
"arxiv_id": "neurips/2307.04204",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.04204/cifar_mean_width64.png",
"caption": "$m=128$",
"source": "neurips/2307.04204/A1_exp.tex",
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"type": "Scatter Plot"
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{
"figure_path": "neurips_figures/2307.04204/multiple_mean_size2.png",
"caption": "$n=4$",
"source": "neurips/2307.04204/A1_exp.tex",
"arxiv_id": "neurips/2307.04204",
"type": "Line Chart"
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"figure_path": "neurips_figures/2307.04204/single_depth10_width64.png",
"caption": "$m=128$",
"source": "neurips/2307.04204/A1_exp.tex",
"arxiv_id": "neurips/2307.04204",
"type": "Scatter Plot"
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"figure_path": "neurips_figures/2307.04204/single_depth3_width64.png",
"caption": "$m=128$",
"source": "neurips/2307.04204/A1_exp.tex",
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"type": "Scatter Plot"
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"figure_path": "neurips_figures/2307.04204/single_depth3_width256_scale0.5.png",
"caption": "$\\alpha=1.0$",
"source": "neurips/2307.04204/A1_exp.tex",
"arxiv_id": "neurips/2307.04204",
"type": "Line Chart"
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{
"figure_path": "neurips_figures/2307.04204/depth3_width64_scale5.png",
"caption": "$m=256$, $L=3$",
"source": "neurips/2307.04204/3_GDalign.tex",
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"type": "Scatter Plot"
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"figure_path": "neurips_figures/2307.04204/linear_depth2_width256_scale3.png",
"caption": "$\\alpha=10$",
"source": "neurips/2307.04204/3_GDalign.tex",
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"type": "Scatter Plot"
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"figure_path": "kdd_figures/2307.10213/Figure_2.jpg",
"caption": "Confusion matrix for hate speech detection.",
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"arxiv_id": "kdd/2307.10213",
"type": "Heat Map"
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{
"figure_path": "kdd_figures/2307.00653/MainGraph.png",
"caption": "Comparison of NLM and backtracking convergence time",
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"arxiv_id": "kdd/2307.00653",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.14126/non-defense.png",
"caption": "Comparison of traffic forecasting model performance under adversarial attack. (a) Results without defense, showing biased predictions under attack. (b) Results with adversarial training, showing improved robustness and similar predictions to original model.",
"source": "kdd/2306.14126/sample-lualatex.tex",
"arxiv_id": "kdd/2306.14126",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.10079/score1.png",
"caption": "The tuning results of prediction threshold $\\pi$ on the two datasets.",
"source": "kdd/2306.10079/042exp_correct.tex",
"arxiv_id": "kdd/2306.10079",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.10079/image_encoder.png",
"caption": "The precision of top-3/5 tags for the POIs on on MPTD2 predicted by the M3PTs with different image encoders.",
"source": "kdd/2306.10079/042exp_correct.tex",
"arxiv_id": "kdd/2306.10079",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2306.09364/patch_embedd_fig.jpg",
"caption": "Correlation between Patch time-series and its associated embeddings.",
"source": "kdd/2306.09364/main.tex",
"arxiv_id": "kdd/2306.09364",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.04643/validation_sse.png",
"caption": "Cross Validation SSE.",
"source": "kdd/2306.04643/main.tex",
"arxiv_id": "kdd/2306.04643",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.04643/PCA.png",
"caption": "PCA Visualization of the Clustering Result.",
"source": "kdd/2306.04643/main.tex",
"arxiv_id": "kdd/2306.04643",
"type": "Scatter Plot"
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"figure_path": "kdd_figures/2306.04643/select_k.png",
"caption": "WCSS and DBI vs Number of Cluster Plot.",
"source": "kdd/2306.04643/main.tex",
"arxiv_id": "kdd/2306.04643",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.04643/benford.png",
"caption": "Our observation of the dataset's first digit distribution vs. expectation of the distribution according to Benford's law.",
"source": "kdd/2306.04643/main.tex",
"arxiv_id": "kdd/2306.04643",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2306.04370/time.png",
"caption": "Proportion distribution of clicks between users and five major food categories in four different time periods. Note that the sum of the proportions of the five food categories in each time period is equal to 1.",
"source": "kdd/2306.04370/Introduction.tex",
"arxiv_id": "kdd/2306.04370",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2306.04370/large_time_graph_auc.png",
"caption": "Impact of period-varying modeling on results for different time periods on AUC metric in MT-large dataset.",
"source": "kdd/2306.04370/Appendix.tex",
"arxiv_id": "kdd/2306.04370",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2306.04370/small_time_graph_auc.png",
"caption": "Impact of period-varying modeling on results for different time periods on AUC metric in MT-small dataset.",
"source": "kdd/2306.04370/Appendix.tex",
"arxiv_id": "kdd/2306.04370",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2306.04039/mips_vs_mol_gpu_util_mem_v1.png",
"caption": "Infra efficiency in production: GPU utilization and peak memory scaling with serving FLOPs.",
"source": "kdd/2306.04039/main.tex",
"arxiv_id": "kdd/2306.04039",
"type": "Line Chart"
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"figure_path": "kdd_figures/2306.02679/dbp_viz.png",
"caption": "Visualization of the entity embeddings of five popular types in \\dbpfivem.",
"source": "kdd/2306.02679/sect7_app.tex",
"arxiv_id": "kdd/2306.02679",
"type": "Scatter Plot"
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"figure_path": "kdd_figures/2305.18885/TA.png",
"caption": "TA",
"source": "kdd/2305.18885/0.0.KDD2023_full.tex",
"arxiv_id": "kdd/2305.18885",
"type": "Bar Chart"
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"caption": "",
"source": "kdd/2305.18885/0.0.KDD2023_full.tex",
"arxiv_id": "kdd/2305.18885",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2304.01166/char21.png",
"caption": "Correlation matrix after a proposed feature extraction method",
"source": "kdd/2304.01166/Experiment_Details.tex",
"arxiv_id": "kdd/2304.01166",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2304.01166/chart1.png",
"caption": "Results of the proposed model",
"source": "kdd/2304.01166/Experiment_Details.tex",
"arxiv_id": "kdd/2304.01166",
"type": "Bar Chart"
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{
"figure_path": "kdd_figures/2302.13522/tiny_gpu_stats.png",
"caption": "GPU streaming multiprocessor utilization for IGB-tiny.",
"source": "kdd/2302.13522/experiment.tex",
"arxiv_id": "kdd/2302.13522",
"type": "Line Chart"
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{
"figure_path": "kdd_figures/2302.13522/full_gpu_stats.png",
"caption": "GPU streaming multiprocessor utilization for IGB.",
"source": "kdd/2302.13522/experiment.tex",
"arxiv_id": "kdd/2302.13522",
"type": "Histogram"
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"figure_path": "kdd_figures/2302.11159/dtw5.png",
"caption": "Different Adjacency Graphs",
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"arxiv_id": "kdd/2302.11159",
"type": "Heat Map"
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{
"figure_path": "kdd_figures/2302.09178/divergence.png",
"caption": "Example of loss divergence in our model and its impact on training loss (top) and AUC (bottom). In this example, model-a's loss micro-diverged then recovered, whereas model-b's loss fully-diverged.",
"source": "kdd/2302.09178/background.tex",
"arxiv_id": "kdd/2302.09178",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2302.09178/xformer-comparison.png",
"caption": "A comparison of AdamW, Adagrad and Adagrad with Clippy on the task for English to German translation.",
"source": "kdd/2302.09178/appendix.tex",
"arxiv_id": "kdd/2302.09178",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2302.05549/running_time_v2.png",
"caption": "Running Time of DistMS in Spark on a 10 Million dataset",
"source": "kdd/2302.05549/sample-sigconf.tex",
"arxiv_id": "kdd/2302.05549",
"type": null
},
{
"figure_path": "kdd_figures/2302.02592/figure7a.png",
"caption": "Effects of maximizing traffic value.",
"source": "kdd/2302.02592/src-projname.tex",
"arxiv_id": "kdd/2302.02592",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2302.02592/figure6.png",
"caption": "At different training episodes, the trends of cumulative reward at all 288 decision steps.",
"source": "kdd/2302.02592/src-projname.tex",
"arxiv_id": "kdd/2302.02592",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2302.02592/figure_5b.png",
"caption": "The curves of cumulative reward and delivery completion rate during training (30,000 episodes).",
"source": "kdd/2302.02592/src-projname.tex",
"arxiv_id": "kdd/2302.02592",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2302.01416/mae_rmse_per_domain.png",
"caption": "RMSE(top) and MAE(bottom) of GLM(blue) and our multimodal neural network(orange) evaluated on each domain.",
"source": "kdd/2302.01416/preprint.tex",
"arxiv_id": "kdd/2302.01416",
"type": "Bar Chart"
},
{
"figure_path": "kdd_figures/2210.14309/gammaNDCG.png",
"caption": "The NDCG@50 of head, tail and overall performance w.r.t different $\\gamma$.",
"source": "kdd/2210.14309/sample-sigconf.tex",
"arxiv_id": "kdd/2210.14309",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2210.14309/ExpertGate.png",
"caption": "Gate value of memorization-focused and generalization-focused experts for head (blue) and tail (orange) items. In CDN, head items put more weights on memorization-focused experts while tail items put more weights on generalization-focused experts",
"source": "kdd/2210.14309/sample-sigconf.tex",
"arxiv_id": "kdd/2210.14309",
"type": "Bar Chart"
},
{
"figure_path": "kdd_figures/2205.10053/pubmed_alpha.png",
"caption": "Effect of $\\alpha$.",
"source": "kdd/2205.10053/main.tex",
"arxiv_id": "kdd/2205.10053",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2112.06668/dropout_rate.png",
"caption": "The impact of different dropout rates for CT4Rec and SASRec* on the Beauty dataset.",
"source": "kdd/2112.06668/4-Results.tex",
"arxiv_id": "kdd/2112.06668",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2106.04486/graph_plot_topK_time.png",
"caption": "(a) \\methodgraph-K scales linearly with factor $K$. (b) Linear scalability with number of hash functions. (c) Linear scalability with number of edges.",
"source": "kdd/2106.04486/sample-sigconf.tex",
"arxiv_id": "kdd/2106.04486",
"type": "Line Chart"
},
{
"figure_path": "kdd_figures/2106.04486/edge_plot_hashfn_time.png",
"caption": "(a) Linear scalability with number of hash functions. (b) Linear scalability with number of edges.",
"source": "kdd/2106.04486/sample-sigconf.tex",
"arxiv_id": "kdd/2106.04486",
"type": null
},
{
"figure_path": "icra_figures/2210.14055/blocks_world_comparison_latest_errbars_withgreedy.png",
"caption": "Figure shows solve rate as a function of planning time. Table reports percentage of problems solved within 90 second timeout. All variants of $\\lifted$ use the LevinTS priority function with the learned policy. We report the mean and standard deviation across 5 random seeds for each method.",
"source": "icra/2210.14055/ICRA_2023_-_Camera_Ready/root.tex",
"arxiv_id": "icra/2210.14055",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2210.14055/blocks_world_comparison_latest_errbars_withgreedy.png",
"caption": "Figure shows solve rate as a function of planning time. Table reports percentage of problems solved within 90 second timeout. All variants of $\\lifted$ use the LevinTS priority function with the learned policy. We report the mean and standard deviation across 5 random seeds for each method.",
"source": "icra/2210.14055/root.tex",
"arxiv_id": "icra/2210.14055",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2209.08258/Prediction_Comparison.png",
"caption": "Comparison of the failure ratio between our proposed method and the linear predictor in three different simulation environments.",
"source": "icra/2209.08258/main.tex",
"arxiv_id": "icra/2209.08258",
"type": "Bar Chart"
},
{
"figure_path": "icra_figures/2209.07003/vel_plot.png",
"caption": "The velocity profile of a physical flight test for each axis. The velocity data are obtained using the onboard visual-inertial state estimation.",
"source": "icra/2209.07003/main.tex",
"arxiv_id": "icra/2209.07003",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2209.07003/runtime.png",
"caption": "The recorded average runtime for each component of our system. The entire system is able to run in real-time by the onboard computer.",
"source": "icra/2209.07003/main.tex",
"arxiv_id": "icra/2209.07003",
"type": "Bar Chart"
},
{
"figure_path": "icra_figures/2209.07003/guide_point.png",
"caption": "Illustration of circle-based guide-point assignment. For the given collision trajectory, we first find the collision control points and search the collision-free paths. The guide points shown as purple points are the intersections between circle-based raycasting and the searched paths.",
"source": "icra/2209.07003/main.tex",
"arxiv_id": "icra/2209.07003",
"type": null
},
{
"figure_path": "icra_figures/2209.04346/PacejkaFit.png",
"caption": "Data points obtained from the steady-state cornering experiment and the resulting model fit of the Pacejka model. Outliers are marked red, inliers green, and the model prediction is shown in blue for a fixed load of 16.4\\,N on the front axle and 18.6\\,N on the rear axle.",
"source": "icra/2209.04346/main.tex",
"arxiv_id": "icra/2209.04346",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2207.00721/TPUvsPP.png",
"caption": "The comparison between end-effectors that are printed with TPU (a and b) and PP (c and d). Radius of the circle varies between 30 mm and 10 mm, colors indicating different radii (a and c). As the distance between the stylus pen and the touchpad reduces, the shape changes due to the compliance of the end-effector (b and d). The effect of the material is clearly observed. Since the PP end-effector cannot conform to the environment as well as TPU end-effector, it snapped off of the forearms, hence resulting with a non-uniform shape (green line).",
"source": "icra/2207.00721/root.tex",
"arxiv_id": "icra/2207.00721",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2207.00721/rewards_combined.png",
"caption": "Gaussian plots of rewards across different robots.",
"source": "icra/2207.00721/root.tex",
"arxiv_id": "icra/2207.00721",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2207.00721/REPS_2_rho_theta_policy_0.png",
"caption": "Convergence of parameters for a single Robot.",
"source": "icra/2207.00721/root.tex",
"arxiv_id": "icra/2207.00721",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2207.00721/rewards_combined.png",
"caption": "Gaussian plots for rewards over all the experiments over the 3 robots. There is significant overlap in the convergence trends across all 3 robots.",
"source": "icra/2207.00721/RoboMath.tex",
"arxiv_id": "icra/2207.00721",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2207.00721/REPS_2_rho_theta_policy_0.png",
"caption": "Convergence of Gaussian curves over training epochs for all the parameters of a robot in 1 experiment.",
"source": "icra/2207.00721/RoboMath.tex",
"arxiv_id": "icra/2207.00721",
"type": "Line Chart"
},
{
"figure_path": "icra_figures/2204.03698/dynamics.png",
"caption": "Open loop trajectories of two ring finger joint angles $q_1$ and $q_3$ following previously recorded target positions of a trained policy. In blue we show $15$ individual real trajectories across three robot initializations, and in red $50$ individual simulation trajectories each with dynamics sampled from the domain randomization distribution used for training. The dashed lines indicate the range of the geometric randomization.",
"source": "icra/2204.03698/2022-icra-manipulation.tex",
"arxiv_id": "icra/2204.03698",