Fix spacing of references

benchmarking.qmd

@@ -285,7 +285,7 @@ Here are some original works that laid the fundamental groundwork for developing
 
 *[MLPerf Training Benchmark](https://github.com/mlcommons/training)*
 
-MLPerf is a suite of benchmarks designed to measure the performance of machine learning hardware, software, and services. The MLPerf Training benchmark[@mattson2020mlperf] focuses on the time it takes to train models to a target quality metric. It includes a diverse set of workloads, such as image classification, object detection, translation, and reinforcement learning.
+MLPerf is a suite of benchmarks designed to measure the performance of machine learning hardware, software, and services. The MLPerf Training benchmark [@mattson2020mlperf] focuses on the time it takes to train models to a target quality metric. It includes a diverse set of workloads, such as image classification, object detection, translation, and reinforcement learning.
 
 Metrics:
 
@@ -295,7 +295,7 @@ Metrics:
 
 *[DAWNBench](https://dawn.cs.stanford.edu/benchmark/)*
 
-DAWNBench[@coleman2017dawnbench] is a benchmark suite that focuses on end-to-end deep learning training time and inference performance. It includes common tasks such as image classification and question answering.
+DAWNBench [@coleman2017dawnbench] is a benchmark suite that focuses on end-to-end deep learning training time and inference performance. It includes common tasks such as image classification and question answering.
 
 Metrics:
 
@@ -305,7 +305,7 @@ Metrics:
 
 *[Fathom](https://github.com/rdadolf/fathom)*
 
-Fathom[@adolf2016fathom] is a benchmark from Harvard University that includes a diverse set of workloads to evaluate the performance of deep learning models. It includes common tasks such as image classification, speech recognition, and language modeling.
+Fathom [@adolf2016fathom] is a benchmark from Harvard University that includes a diverse set of workloads to evaluate the performance of deep learning models. It includes common tasks such as image classification, speech recognition, and language modeling.
 
 Metrics:
 
@@ -483,7 +483,7 @@ But of all these, perhaps the most important challenge is dealing with benchmark
 
 #### Hardware Lottery
 
-The ["hardware lottery"](https://arxiv.org/abs/2009.06489) in benchmarking machine learning systems refers to the situation where the success or efficiency of a machine learning model is significantly influenced by the compatibility of the model with the underlying hardware[@chu2021discovering]. In other words, some models perform exceptionally well because they are a good fit for the particular characteristics or capabilities of the hardware on which they are run, rather than because they are intrinsically superior models. Unfortunately, the hardware used is often omitted from papers or given only brief mentions, making reproducing results difficult if not impossible.
+The ["hardware lottery"](https://arxiv.org/abs/2009.06489) in benchmarking machine learning systems refers to the situation where the success or efficiency of a machine learning model is significantly influenced by the compatibility of the model with the underlying hardware [@chu2021discovering]. In other words, some models perform exceptionally well because they are a good fit for the particular characteristics or capabilities of the hardware on which they are run, rather than because they are intrinsically superior models. Unfortunately, the hardware used is often omitted from papers or given only brief mentions, making reproducing results difficult if not impossible.
 
 For instance, certain machine learning models may be designed and optimized to take advantage of parallel processing capabilities of specific hardware accelerators, such as Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). As a result, these models might show superior performance when benchmarked on such hardware, compared to other models that are not optimized for the hardware.
 
@@ -670,11 +670,11 @@ Lastly, the contributions of academic and research institutions cannot be overst
 
 As machine learning models become more sophisticated, so do the benchmarks required to accurately assess them. There are several emerging benchmarks and datasets that are gaining popularity due to their ability to evaluate models in more complex and realistic scenarios:
 
-**Multimodal Datasets:** These datasets contain multiple types of data, such as text, images, and audio, to better represent real-world situations. An example is the VQA (Visual Question Answering) dataset[@antol2015vqa], where models are tested on their ability to answer text-based questions about images.
+**Multimodal Datasets:** These datasets contain multiple types of data, such as text, images, and audio, to better represent real-world situations. An example is the VQA (Visual Question Answering) dataset [@antol2015vqa], where models are tested on their ability to answer text-based questions about images.
 
 **Fairness and Bias Evaluation:** There is an increasing focus on creating benchmarks that assess the fairness and bias of machine learning models. Examples include the [AI Fairness 360](https://ai-fairness-360.org/) toolkit, which offers a comprehensive set of metrics and datasets for evaluating bias in models.
 
-**Out-of-Distribution Generalization**: Testing how well models perform on data that is different from the original training distribution. This evaluates the model's ability to generalize to new, unseen data. Example benchmarks are Wilds[@koh2021wilds], RxRx, and ANC-Bench.
+**Out-of-Distribution Generalization**: Testing how well models perform on data that is different from the original training distribution. This evaluates the model's ability to generalize to new, unseen data. Example benchmarks are Wilds [@koh2021wilds], RxRx, and ANC-Bench.
 
 **Adversarial Robustness:** Evaluating model performance under adversarial attacks or perturbations to the input data. This tests the model's robustness. Example benchmarks are ImageNet-A[@hendrycks2021natural], ImageNet-C[@xie2020adversarial], and CIFAR-10.1.
 
@@ -738,7 +738,7 @@ The shift towards data-centric AI represents a significant paradigm shift. By pr
 
 ### Benchmarking Data
 
-Data benchmarking aims to evaluate common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset[@gaviria2022dollar]. In its simplest form, data benchmarking aims to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.
+Data benchmarking aims to evaluate common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset [@gaviria2022dollar]. In its simplest form, data benchmarking aims to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.
 
 Data-centric techniques continue to gain attention in benchmarking, especially as foundation models are increasingly trained on self-supervised objectives. Compared to smaller datasets like Imagenet-1K, massive datasets commonly used in self-supervised learning such as Common Crawl, OpenImages, and LAION-5B contain an order of magnitude higher amounts of noise, duplicates, bias, and potentially offensive data.