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05/04/2023
Cuttlefish: Low-Rank Model Training without All the Tuning
Recent research has shown that training low-rank neural networks can eff...
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02/08/2023
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
The canonical formulation of federated learning treats it as a distribut...
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01/06/2023
Does compressing activations help model parallel training?
Large-scale Transformer models are known for their exceptional performan...
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12/09/2022
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the ...
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10/09/2022
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
Data-to-text generation is challenging due to the great variety of the i...
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07/30/2022
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Few-shot object detection has been extensively investigated by incorpora...
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07/28/2022
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion
The recently proposed DEtection TRansformer (DETR) has established a ful...
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07/18/2022
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
We introduce the initial release of our software Robustar, which aims to...
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07/18/2022
MRCLens: an MRC Dataset Bias Detection Toolkit
Many recent neural models have shown remarkable empirical results in Mac...
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06/28/2022
BertNet: Harvesting Knowledge Graphs from Pretrained Language Models
Symbolic knowledge graphs (KGs) have been constructed either by expensiv...
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06/04/2022
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Data augmentation has been proven to be an effective technique for devel...
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05/25/2022
RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning
Prompting has shown impressive success in enabling large pretrained lang...
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04/09/2022
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has ...
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02/02/2022
Can Transformers be Strong Treatment Effect Estimators?
In this paper, we develop a general framework based on the Transformer a...
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11/27/2021
Towards Principled Disentanglement for Domain Generalization
A fundamental challenge for machine learning models is generalizing to o...
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11/01/2021
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) ...
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10/11/2021
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
In the genome biology research, regulatory genome modeling is an importa...
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10/06/2021
Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid
As a scalable data-driven approach, multi-agent reinforcement learning (...
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09/14/2021
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
Natural language generation (NLG) spans a broad range of tasks, each of ...
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09/10/2021
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Meta-learning has achieved great success in leveraging the historical le...
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08/17/2021
Panoramic Learning with A Standardized Machine Learning Formalism
Machine Learning (ML) is about computational methods that enable machine...
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06/14/2021
Text Generation with Efficient (Soft) Q-Learning
Maximum likelihood estimation (MLE) is the predominant algorithm for tra...
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05/30/2021
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Automatic math problem solving has recently attracted increasing attenti...
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03/02/2021
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domai...
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11/28/2020
Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels
The data-driven nature of deep learning models for semantic segmentation...
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11/25/2020
Squared ℓ_2 Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving th...
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10/23/2020
Iterative Graph Self-Distillation
How to discriminatively vectorize graphs is a fundamental challenge that...
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10/20/2020
Word Shape Matters: Robust Machine Translation with Visual Embedding
Neural machine translation has achieved remarkable empirical performance...
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10/14/2020
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
Given a document and a target aspect (e.g., a topic of interest), aspect...
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07/05/2020
Self-Challenging Improves Cross-Domain Generalization
Convolutional Neural Networks (CNN) conduct image classification by acti...
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07/02/2020
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks
We examine Dropout through the perspective of interactions: learned effe...
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06/28/2020
Progressive Generation of Long Text
Large-scale language models pretrained on massive corpora of text, such ...
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06/12/2020
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Despite success on a wide range of problems related to vision, generativ...
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01/15/2020
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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10/28/2019
Learning Data Manipulation for Augmentation and Weighting
Manipulating data, such as weighting data examples or augmenting with ne...
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10/15/2019
Learning Sample-Specific Models with Low-Rank Personalized Regression
Modern applications of machine learning (ML) deal with increasingly hete...
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09/29/2019
Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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08/05/2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
We describe ChemBO, a Bayesian Optimization framework for generating and...
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05/29/2019
Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i.i.d. data, convolutional ne...
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05/28/2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image ...
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05/28/2019
Adversarial Domain Adaptation Being Aware of Class Relationships
Adversarial training is a useful approach to promote the learning of tra...
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05/28/2019
Target-Guided Open-Domain Conversation
Many real-world open-domain conversation applications have specific goal...
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03/25/2019
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Generating long and semantic-coherent reports to describe medical images...
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03/15/2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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01/24/2019
Theoretically Principled Trade-off between Robustness and Accuracy
We identify a trade-off between robustness and accuracy that serves as a...
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11/19/2018
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
We study the problem of alleviating the instability issue in the GAN tra...
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11/13/2018
Discourse in Multimedia: A Case Study in Information Extraction
To ensure readability, text is often written and presented with due form...
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10/17/2018
Fault Tolerance in Iterative-Convergent Machine Learning
Machine learning (ML) training algorithms often possess an inherent self...
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10/08/2018
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Many distributed machine learning (ML) systems adopt the non-synchronous...
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09/10/2018
