b'Eric P Xing'

research

∙ 05/04/2023

Cuttlefish: Low-Rank Model Training without All the Tuning

Recent research has shown that training low-rank neural networks can eff...

0 Hongyi Wang, et al. ∙

research

∙ 02/08/2023

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach

The canonical formulation of federated learning treats it as a distribut...

0 Han Guo, et al. ∙

research

∙ 01/06/2023

Does compressing activations help model parallel training?

Large-scale Transformer models are known for their exceptional performan...

0 Song Bian, et al. ∙

research

∙ 12/09/2022

Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

Mix-up training approaches have proven to be effective in improving the ...

0 Minh-Long Luu, et al. ∙

research

∙ 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...

0 Jiannan Xiang, et al. ∙

research

∙ 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...

29 Gongjie Zhang, et al. ∙

research

∙ 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...

14 Gongjie Zhang, et al. ∙

research

∙ 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...

0 Chonghan Chen, et al. ∙

research

∙ 07/18/2022

MRCLens: an MRC Dataset Bias Detection Toolkit

Many recent neural models have shown remarkable empirical results in Mac...

23 Yifan Zhong, et al. ∙

research

∙ 06/28/2022

BertNet: Harvesting Knowledge Graphs from Pretrained Language Models

Symbolic knowledge graphs (KGs) have been constructed either by expensiv...

22 Shibo Hao, et al. ∙

research

∙ 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...

25 Haohan Wang, et al. ∙

research

∙ 05/25/2022

RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning

Prompting has shown impressive success in enabling large pretrained lang...

9 Mingkai Deng, et al. ∙

research

∙ 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 ...

4 Zeyi Huang, et al. ∙

research

∙ 02/02/2022

Can Transformers be Strong Treatment Effect Estimators?

In this paper, we develop a general framework based on the Transformer a...

14 Yi-Fan Zhang, et al. ∙

research

∙ 11/27/2021

Towards Principled Disentanglement for Domain Generalization

A fundamental challenge for machine learning models is generalizing to o...

8 Hanlin Zhang, et al. ∙

research

∙ 11/01/2021

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) ...

4 Ben Lengerich, et al. ∙

research

∙ 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...

15 Shentong Mo, et al. ∙

research

∙ 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 (...

12 Zhaoming Qin, et al. ∙

research

∙ 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 ...

31 Mingkai Deng, et al. ∙

research

∙ 09/10/2021

Knowledge-Aware Meta-learning for Low-Resource Text Classification

Meta-learning has achieved great success in leveraging the historical le...

20 Huaxiu Yao, et al. ∙

research

∙ 08/17/2021

Panoramic Learning with A Standardized Machine Learning Formalism

Machine Learning (ML) is about computational methods that enable machine...

7 Zhiting Hu, et al. ∙

research

∙ 06/14/2021

Text Generation with Efficient (Soft) Q-Learning

Maximum likelihood estimation (MLE) is the predominant algorithm for tra...

2 Han Guo, et al. ∙

research

∙ 05/30/2021

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

Automatic math problem solving has recently attracted increasing attenti...

25 Jiaqi Chen, et al. ∙

research

∙ 03/02/2021

A Data-Centric Framework for Composable NLP Workflows

Empirical natural language processing (NLP) systems in application domai...

17 Zhengzhong Liu, et al. ∙

research

∙ 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...

15 Nanqing Dong, et al. ∙

research

∙ 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...

0 Haohan Wang, et al. ∙

research

∙ 10/23/2020

Iterative Graph Self-Distillation

How to discriminatively vectorize graphs is a fundamental challenge that...

2 Hanlin Zhang, et al. ∙

research

∙ 10/20/2020

Word Shape Matters: Robust Machine Translation with Visual Embedding

Neural machine translation has achieved remarkable empirical performance...

6 Haohan Wang, et al. ∙

research

∙ 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...

0 Bowen Tan, et al. ∙

research

∙ 07/05/2020

Self-Challenging Improves Cross-Domain Generalization

Convolutional Neural Networks (CNN) conduct image classification by acti...

0 Zeyi Huang, et al. ∙

research

∙ 07/02/2020

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks

We examine Dropout through the perspective of interactions: learned effe...

0 Benjamin Lengerich, et al. ∙

research

∙ 06/28/2020

Progressive Generation of Long Text

Large-scale language models pretrained on massive corpora of text, such ...

0 Bowen Tan, et al. ∙

research

∙ 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...

15 Yue Wu, et al. ∙

research

∙ 01/15/2020

Distributed, partially collapsed MCMC for Bayesian Nonparametrics

Bayesian nonparametric (BNP) models provide elegant methods for discover...

33 Avinava Dubey, et al. ∙

research

∙ 10/28/2019

Learning Data Manipulation for Augmentation and Weighting

Manipulating data, such as weighting data examples or augmenting with ne...

30 Zhiting Hu, et al. ∙

research

∙ 10/15/2019

Learning Sample-Specific Models with Low-Rank Personalized Regression

Modern applications of machine learning (ML) deal with increasingly hete...

37 Benjamin Lengerich, et al. ∙

research

∙ 09/29/2019

Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...

18 Xun Zheng, et al. ∙

research

∙ 08/05/2019

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...

0 Ksenia Korovina, et al. ∙

research

∙ 05/29/2019

Learning Robust Global Representations by Penalizing Local Predictive Power

Despite their renowned predictive power on i.i.d. data, convolutional ne...

4 Haohan Wang, et al. ∙

research

∙ 05/28/2019

High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

We investigate the relationship between the frequency spectrum of image ...

0 Haohan Wang, et al. ∙

research

∙ 05/28/2019

Adversarial Domain Adaptation Being Aware of Class Relationships

Adversarial training is a useful approach to promote the learning of tra...

1 Zeya Wang, et al. ∙

research

∙ 05/28/2019

Target-Guided Open-Domain Conversation

Many real-world open-domain conversation applications have specific goal...

0 Jianheng Tang, et al. ∙

research

∙ 03/25/2019

Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

Generating long and semantic-coherent reports to describe medical images...

8 Christy Y. Li, et al. ∙

research

∙ 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...

14 Kirthevasan Kandasamy, et al. ∙

research

∙ 01/24/2019

Theoretically Principled Trade-off between Robustness and Accuracy

We identify a trade-off between robustness and accuracy that serves as a...

5 Hongyang Zhang, et al. ∙

research

∙ 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...

8 Hongyang Zhang, et al. ∙

research

∙ 11/13/2018

Discourse in Multimedia: A Case Study in Information Extraction

To ensure readability, text is often written and presented with due form...

0 Mrinmaya Sachan, et al. ∙

research

∙ 10/17/2018

Fault Tolerance in Iterative-Convergent Machine Learning

Machine learning (ML) training algorithms often possess an inherent self...

0 Aurick Qiao, et al. ∙

research

∙ 10/08/2018

Toward Understanding the Impact of Staleness in Distributed Machine Learning

Many distributed machine learning (ML) systems adopt the non-synchronous...

2 Wei Dai, et al. ∙

research

∙ 09/10/2018

Sample Complexity of Nonparametric Semi-Supervised Learning

We study the sample complexity of semi-supervised learning (SSL) and int...

0 Chen Dan, et al. ∙

Eric P Xing

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