b'Eric Xing'

research

∙ 07/02/2023

Defending Against Malicious Behaviors in Federated Learning with Blockchain

In the era of deep learning, federated learning (FL) presents a promisin...

0 Nanqing Dong, et al. ∙

research

∙ 06/22/2023

Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

We present a new dataset condensation framework termed Squeeze, Recover ...

0 Zeyuan Yin, et al. ∙

research

∙ 06/13/2023

One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

We present Generalized LoRA (GLoRA), an advanced approach for universal ...

0 Arnav Chavan, et al. ∙

research

∙ 05/23/2023

3D Open-vocabulary Segmentation with Foundation Models

Open-vocabulary segmentation of 3D scenes is a fundamental function of h...

0 Kunhao Liu, et al. ∙

research

∙ 05/05/2023

Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming

Pre-trained large language models (LMs) struggle to perform logical reas...

0 Hanlin Zhang, et al. ∙

research

∙ 04/03/2023

3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

Robust point cloud parsing under all-weather conditions is crucial to le...

0 Aoran Xiao, et al. ∙

research

∙ 03/30/2023

KD-DLGAN: Data Limited Image Generation via Knowledge Distillation

Generative Adversarial Networks (GANs) rely heavily on large-scale train...

0 Kaiwen Cui, et al. ∙

research

∙ 03/19/2023

StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

3D style transfer aims to render stylized novel views of a 3D scene with...

0 Kunhao Liu, et al. ∙

research

∙ 03/08/2023

Memory-adaptive Depth-wise Heterogenous Federated Learning

Federated learning is a promising paradigm that allows multiple clients ...

0 Kai Zhang, et al. ∙

research

∙ 12/16/2022

The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning

Pre-trained language models (LMs) have shown remarkable reasoning perfor...

0 Hanlin Zhang, et al. ∙

research

∙ 10/20/2022

MixMask: Revisiting Masked Siamese Self-supervised Learning in Asymmetric Distance

Recent advances in self-supervised learning integrate Masked Modeling an...

0 Kirill Vishniakov, et al. ∙

research

∙ 01/30/2022

Stochastic Neural Networks with Infinite Width are Deterministic

This work theoretically studies stochastic neural networks, a main type ...

8 Liu Ziyin, et al. ∙

research

∙ 01/03/2022

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

This paper explores the feasibility of finding an optimal sub-model from...

17 Arnav Chavan, et al. ∙

research

∙ 12/03/2021

Data-Free Neural Architecture Search via Recursive Label Calibration

This paper aims to explore the feasibility of neural architecture search...

1 Zechun Liu, et al. ∙

research

∙ 12/02/2021

A Fast Knowledge Distillation Framework for Visual Recognition

While Knowledge Distillation (KD) has been recognized as a useful tool i...

33 Zhiqiang Shen, et al. ∙

research

∙ 11/29/2021

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

The nonuniform quantization strategy for compressing neural networks usu...

11 Zechun Liu, et al. ∙

research

∙ 11/11/2021

Learning from Mistakes – A Framework for Neural Architecture Search

Learning from one's mistakes is an effective human learning technique wh...

58 Bhanu Garg, et al. ∙

research

∙ 11/09/2021

Sliced Recursive Transformer

We present a neat yet effective recursive operation on vision transforme...

8 Zhiqiang Shen, et al. ∙

research

∙ 11/05/2021

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features

Machine learning has demonstrated remarkable prediction accuracy over i....

4 Haohan Wang, et al. ∙

research

∙ 11/05/2021

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

Motivated by empirical arguments that are well-known from the genome-wid...

0 Haohan Wang, et al. ∙

research

∙ 11/03/2021

Multi-task Learning of Order-Consistent Causal Graphs

We consider the problem of discovering K related Gaussian directed acycl...

8 Xinshi Chen, et al. ∙

research

∙ 06/17/2021

Prototypical Graph Contrastive Learning

Graph-level representations are critical in various real-world applicati...

5 Shuai Lin, et al. ∙

research

∙ 05/20/2021

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

Entanglement is a physical phenomenon, which has fueled recent successes...

23 Nanqing Dong, et al. ∙

research

∙ 02/24/2021

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography

Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool t...

6 Xuefeng Du, et al. ∙

research

∙ 01/30/2021

On Data Efficiency of Meta-learning

Meta-learning has enabled learning statistical models that can be quickl...

8 Maruan Al-Shedivat, et al. ∙

research

∙ 10/11/2020

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

Federated learning is typically approached as an optimization problem, w...

4 Maruan Al-Shedivat, et al. ∙

research

∙ 06/17/2020

XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports

To effectively train medical students to become qualified radiologists, ...

20 Xingyi Yang, et al. ∙

research

∙ 05/11/2020

On the Generation of Medical Dialogues for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symp...

0 Wenmian Yang, et al. ∙

research

∙ 04/26/2020

Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Chest X-Ray (CXR) images are commonly used for clinical screening and di...

4 Baoyu Jing, et al. ∙

research

∙ 04/07/2020

Learning from Imperfect Annotations

Many machine learning systems today are trained on large amounts of huma...

6 Emmanouil Antonios Platanios, et al. ∙

research

∙ 03/07/2020

PathVQA: 30000+ Questions for Medical Visual Question Answering

Is it possible to develop an "AI Pathologist" to pass the board-certifie...

0 Xuehai He, et al. ∙

research

∙ 09/28/2019

Generalized Zero-shot ICD Coding

The International Classification of Diseases (ICD) is a list of classifi...

0 Congzheng Song, et al. ∙

research

∙ 06/12/2019

Efficient Exploration via State Marginal Matching

To solve tasks with sparse rewards, reinforcement learning algorithms mu...

0 Lisa Lee, et al. ∙

research

∙ 05/31/2019

Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)

Most of the work on interpretable machine learning has focused on design...

5 Gregory Plumb, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 02/19/2019

Explaining a black-box using Deep Variational Information Bottleneck Approach

Briefness and comprehensiveness are necessary in order to give a lot of ...

8 Seojin Bang, et al. ∙

research

∙ 02/18/2019

Regularizing Black-box Models for Improved Interpretability

Most work on interpretability in machine learning has focused on designi...

20 Gregory Plumb, et al. ∙

research

∙ 01/31/2019

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization

Optimizing an expensive-to-query function is a common task in science an...

18 Willie Neiswanger, et al. ∙

research

∙ 01/28/2019

Toward Unsupervised Text Content Manipulation

Controlled generation of text is of high practical use. Recent efforts h...

6 Wentao Wang, et al. ∙

research

∙ 01/01/2019

Text Infilling

Recent years have seen remarkable progress of text generation in differe...

0 Wanrong Zhu, et al. ∙

research

∙ 11/24/2018

Connecting the Dots Between MLE and RL for Sequence Generation

Sequence generation models such as recurrent networks can be trained wit...

0 Bowen Tan, et al. ∙

research

∙ 11/20/2018

Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Extractive summarization is very useful for physicians to better manage ...

0 Xiangan Liu, et al. ∙

research

∙ 11/16/2018

On the Complexity of Exploration in Goal-Driven Navigation

Building agents that can explore their environments intelligently is a c...

10 Maruan Al-Shedivat, et al. ∙

research

∙ 10/31/2018

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-1...

4 Keyang Xu, et al. ∙

research

∙ 10/04/2018

AutoLoss: Learning Discrete Schedules for Alternate Optimization

Many machine learning problems involve iteratively and alternately optim...

8 Haowen Xu, et al. ∙

research

∙ 08/05/2018

Missing Value Imputation Based on Deep Generative Models

Missing values widely exist in many real-world datasets, which hinders t...

14 Hongbao Zhang, et al. ∙

research

∙ 07/10/2018

CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving

Autonomous urban driving navigation with complex multi-agent dynamics is...

2 Xiaodan Liang, et al. ∙

research

∙ 07/10/2018

Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Raven's Progressive Matrices are one of the widely used tests in evaluat...

4 Rajesh Chidambaram, et al. ∙

research

∙ 06/26/2018

Deep Generative Models with Learnable Knowledge Constraints

The broad set of deep generative models (DGMs) has achieved remarkable a...

4 Zhiting Hu, et al. ∙

research

∙ 06/17/2018

Gated Path Planning Networks

Value Iteration Networks (VINs) are effective differentiable path planni...

2 Lisa Lee, et al. ∙

Eric Xing

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