Search Results for author:
Found 128 papers, 58 papers with code
Dive into Deep Learning
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code.
Combating Adversarial Misspellings with Robust Word Recognition
To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier.
A User Simulator for Task-Completion Dialogues
Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator.
Dance Dance Convolution
For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty.
Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i. i. d.
Ranked #104 on
Domain Generalization
on PACS
Deep Active Learning for Named Entity Recognition
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
While classifiers trained on either original or manipulated data alone are sensitive to spurious features (e. g., mentions of genre), models trained on the combined data are less sensitive to this signal.
Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic
Vision-language models (VLMs) are trained for thousands of GPU hours on carefully curated web datasets.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings.
AmazonQA: A Review-Based Question Answering Task
Observing that many questions can be answered based upon the available product reviews, we propose the task of review-based QA.
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Corresponding samples from the real dataset consist of two distinct photographs of the same subject.
Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student).
Mixture Proportion Estimation and PU Learning: A Modern Approach
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier.
RLSbench: Domain Adaptation Under Relaxed Label Shift
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.
Precise Recovery of Latent Vectors from Generative Adversarial Networks
Generative adversarial networks (GANs) transform latent vectors into visually plausible images.
Domain Adaptation under Open Set Label Shift
We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p(x|y) are domain-invariant.
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
However, naively removing all such data could also be wasteful, as it throws away images that contain visual features (in addition to overlapping text).
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation
Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment
Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer), with the most pronounced benefits accruing to low-resource languages.
On the State of the Art in Authorship Attribution and Authorship Verification
Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art.
Learning From Noisy Singly-labeled Data
We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.
Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples.
Learning to Deceive with Attention-Based Explanations
Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing.
Weakly- and Semi-supervised Evidence Extraction
For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness.
Can Neural Network Memorization Be Localized?
Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks $\textit{memorize}$ "hard" examples in the final few layers of the model.
Accelerating Deep Learning by Focusing on the Biggest Losers
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration.
Does Pretraining for Summarization Require Knowledge Transfer?
Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization.
Characterizing Datapoints via Second-Split Forgetting
Popular metrics derived from these dynamics include (i) the epoch at which examples are first correctly classified; (ii) the number of times their predictions flip during training; and (iii) whether their prediction flips if they are held out.
Active Learning with Partial Feedback
While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback).
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions.
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops.
The Mythos of Model Interpretability
First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant.
RATT: Leveraging Unlabeled Data to Guarantee Generalization
To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on the true risk; or (ii) validate empirically on holdout data.
Correcting Exposure Bias for Link Recommendation
Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.
Model-tuning Via Prompts Makes NLP Models Adversarially Robust
Across 5 NLP datasets, 4 adversarial attacks, and 3 different models, MVP improves performance against adversarial substitutions by an average of 8% over standard methods and even outperforms adversarial training-based state-of-art defenses by 3. 5%.
USB: A Unified Summarization Benchmark Across Tasks and Domains
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability.
Entity Projection via Machine Translation for Cross-Lingual NER
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition.
Ranked #4 on
Cross-Lingual NER
on CoNLL 2003
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables.
Local Causal Discovery for Estimating Causal Effects
Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Evaluating Model Performance in Medical Datasets Over Time
Machine learning (ML) models deployed in healthcare systems must face data drawn from continually evolving environments.
Surprising Negative Results for Generative Adversarial Tree Search
We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning.
Evaluating Explanations: How much do explanations from the teacher aid students?
While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated.
Unsupervised Learning under Latent Label Shift
Thus motivated, we introduce a practical algorithm that leverages domain-discriminative models as follows: (i) push examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform non-negative matrix factorization on the discrete data; (iv) combine the recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute $p_d(y|x) \; \forall d$.
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits
While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
In experiments addressing both ELECTRA and RoBERTa models and 10 distinct downstream classification datasets, we observe that self-pretraining rivals standard pretraining on the BookWiki corpus (despite using around $10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$ datasets, respectively.
Auditing Fairness under Unobserved Confounding
A fundamental problem in decision-making systems is the presence of inequity across demographic lines.
Does mitigating ML's impact disparity require treatment disparity?
Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making.
Detecting and Correcting for Label Shift with Black Box Predictors
Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels.
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Through our evaluation, we observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
Domain Adaptation under Missingness Shift
Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable.
Disentangling the Mechanisms Behind Implicit Regularization in SGD
A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training.
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions
We analyze the impact of real-world factors including mud, pose, lighting, and more.
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
Academic research has identified some factors, i. e. computed features of the reported data, that are known through retrospective analysis to outperform the market average.
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
We introduce intrinsic fear (IF), a learned reward shaping that guards DRL agents against periodic catastrophes.
Correction by Projection: Denoising Images with Generative Adversarial Networks
In this paper, we propose to denoise corrupted images by finding the nearest point on the GAN manifold, recovering latent vectors by minimizing distances in image space.
The Doctor Just Won't Accept That!
For the field of interpretable machine learning to advance, we must ask the following questions: What precisely won't various stakeholders accept?
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.
Tensor Regression Networks
First, we introduce Tensor Contraction Layers (TCLs) that reduce the dimensionality of their input while preserving their multilinear structure using tensor contraction.
Predicting Surgery Duration with Neural Heteroscedastic Regression
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking.
Tensor Contraction Layers for Parsimonious Deep Nets
Specifically, we propose the Tensor Contraction Layer (TCL), the first attempt to incorporate tensor contractions as end-to-end trainable neural network layers.
Learning to Diagnose with LSTM Recurrent Neural Networks
We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements.
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements.
Modeling Missing Data in Clinical Time Series with RNNs
For linear models, we show an alternative strategy to capture this signal.
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
Then we train a recurrent neural network that takes as input sequences of pseudo-labeled frames and optimizes an objective that encourages both accuracy on the target frame and consistency across consecutive frames.
Generative Concatenative Nets Jointly Learn to Write and Classify Reviews
A recommender system's basic task is to estimate how users will respond to unseen items.
Stuck in a What? Adventures in Weight Space
As neural networks are typically over-complete, it's easy to show the existence of vast continuous regions through weight space with equal loss.
Efficient Elastic Net Regularization for Sparse Linear Models
This paper provides closed-form updates for the popular squared norm $\ell^2_2$ and elastic net regularizers.
Differential Privacy and Machine Learning: a Survey and Review
The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information.
Troubling Trends in Machine Learning Scholarship
Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms.
Practical Obstacles to Deploying Active Learning
Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget.
Learning Noise-Invariant Representations for Robust Speech Recognition
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
This paper provides a large scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions.
What is the Effect of Importance Weighting in Deep Learning?
Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning.
Estimating Reactions and Recommending Products with Generative Models of Reviews
Natural language approaches that model information like product reviews have proved to be incredibly useful in improving the performance of such methods, as reviews provide valuable auxiliary information that can be used to better estimate latent user preferences and item properties.
Avoiding Catastrophic States with Intrinsic Fear
Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never.
Learning Robust Representations by Projecting Superficial Statistics Out
We test our method on the battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.
Ranked #113 on
Domain Generalization
on PACS
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions
First, noting that in each image the embryo occupies a small subregion, we jointly train a region proposal network with the downstream classifier to isolate the embryo.
Efficient candidate screening under multiple tests and implications for fairness
We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests.
Learning Causal State Representations of Partially Observable Environments
In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).
Estimating brain age based on a healthy population with deep learning and structural MRI
Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases.
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?
For a standard convolutional neural network, optimizing over the input pixels to maximize the score of some target class will generally produce a grainy-looking version of the original image.
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.
Algorithmic Fairness from a Non-ideal Perspective
Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions.
Causal Inference With Selectively Deconfounded Data
In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE.
A Unified View of Label Shift Estimation
Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error.
Estimating Treatment Effects with Observed Confounders and Mediators
We show that it strictly outperforms the backdoor and frontdoor estimators and that this improvement can be unbounded.
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
On a periodic basis, publicly traded companies report fundamentals, financial data including revenue, earnings, debt, among others.
Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances
In this exploratory study, we describe a new dataset consisting of conversation transcripts, post-visit summaries, corresponding supporting evidence (in the transcript), and structured labels.
Explaining The Efficacy of Counterfactually Augmented Data
In attempts to produce ML models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable.
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation.
Fair Machine Learning Under Partial Compliance
If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits?
Rebounding Bandits for Modeling Satiation Effects
Psychological research shows that enjoyment of many goods is subject to satiation, with short-term satisfaction declining after repeated exposures to the same item.
Decoding and Diversity in Machine Translation
This makes human-level BLEU a misleading benchmark in that modern MT systems cannot approach human-level BLEU while simultaneously maintaining human-level translation diversity.
On Proximal Policy Optimization's Heavy-tailed Gradients
In this paper, we present a detailed empirical study to characterize the heavy-tailed nature of the gradients of the PPO surrogate reward function.
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Recent experiments have shown that deep networks can approximate solutions to high-dimensional PDEs, seemingly escaping the curse of dimensionality.
On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk
Because optimizing the coherent risk is difficult in Markov decision processes, recent work tends to focus on the Markov coherent risk (MCR), a time-consistent surrogate.
Off-Policy Risk Assessment in Contextual Bandits
Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data.
Practical Benefits of Feature Feedback Under Distribution Shift
In attempts to develop sample-efficient and interpretable algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback (or rationales) auxiliary annotations provided for training (but not test) instances that highlight salient evidence.
Modeling Attrition in Recommender Systems with Departing Bandits
We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal.
Resolving the Human Subjects Status of Machine Learning's Crowdworkers
We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets.
On the Maximum Hessian Eigenvalue and Generalization
The mechanisms by which certain training interventions, such as increasing learning rates and applying batch normalization, improve the generalization of deep networks remains a mystery.
Supervised Learning with General Risk Functionals
Standard uniform convergence results bound the generalization gap of the expected loss over a hypothesis class.
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
With COVID-19 now pervasive, identification of high-risk individuals is crucial.
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
Real-world deployment of machine learning models is challenging because data evolves over time.
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
We show that if composing a function with Barron norm $b$ with partial derivatives of $L$ produces a function of Barron norm at most $B_L b^p$, the solution to the PDE can be $\epsilon$-approximated in the $L^2$ sense by a function with Barron norm $O\left(\left(dB_L\right)^{\max\{p \log(1/ \epsilon), p^{\log(1/\epsilon)}\}}\right)$.
Model Evaluation in Medical Datasets Over Time
Machine learning models deployed in healthcare systems face data drawn from continually evolving environments.
Meta-Learning Mini-Batch Risk Functionals
Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals.
Users are the North Star for AI Transparency
We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals.
Risk-limiting Financial Audits via Weighted Sampling without Replacement
Next, we develop methods to improve the quality of CSs by incorporating side information about the unknown values associated with each item.
PromptNER: Prompting For Named Entity Recognition
In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot solutions to myriad classic NLP problems.
Ranked #3 on
Zero-shot Named Entity Recognition (NER)
on CrossNER
(using extra training data)
Moral Machine or Tyranny of the Majority?
With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus.
Goodhart's Law Applies to NLP's Explanation Benchmarks
First, we show that we can inflate a model's comprehensiveness and sufficiency scores dramatically without altering its predictions or explanations on in-distribution test inputs.
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains
Medical imaging data is often siloed within hospitals, limiting the amount of data available for specialized model development.
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset
RnD represents a valuable new benchmark to drive innovation in real-world OCR capabilities.
Optical Character Recognition
Optical Character Recognition (OCR)
Deep Equilibrium Based Neural Operators for Steady-State PDEs
Motivated by this observation, we propose FNO-DEQ, a deep equilibrium variant of the FNO architecture that directly solves for the solution of a steady-state PDE as the infinite-depth fixed point of an implicit operator layer using a black-box root solver and differentiates analytically through this fixed point resulting in $\mathcal{O}(1)$ training memory.
TOFU: A Task of Fictitious Unlearning for LLMs
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns.
The Impact of Differential Feature Under-reporting on Algorithmic Fairness
Predictive risk models in the public sector are commonly developed using administrative data that is more complete for subpopulations that more greatly rely on public services.
Red-Teaming for Generative AI: Silver Bullet or Security Theater?
In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks.
Personalized Language Modeling from Personalized Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is the current dominating framework to fine-tune large language models to better align with human preferences.
Contrastive Multiple Instance Learning for Weakly Supervised Person ReID
Through extensive experiments and analysis across three datasets, CMIL not only matches state-of-the-art performance on the large-scale SYSU-30k dataset with fewer assumptions but also consistently outperforms all baselines on the WL-market1501 and Weakly Labeled MUddy racer re-iDentification dataset (WL-MUDD) datasets.
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence
We present GenAudit -- a tool intended to assist fact-checking LLM responses for document-grounded tasks.
