Search Results for author: Eric Xing

Found 113 papers, 42 papers with code

SlimPajama-DC: Understanding Data Combinations for LLM Training

no code implementations19 Sep 2023 Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric Xing

This paper aims to understand the impacts of various data combinations (e. g., web text, wikipedia, github, books) on the training of large language models using SlimPajama.

Defending Against Malicious Behaviors in Federated Learning with Blockchain

no code implementations2 Jul 2023 Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, Yizhe Wen, Shuoying Zhang, William Knottenbelt, Eric Xing

In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.

Federated Learning

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

1 code implementation22 Jun 2023 Zeyuan Yin, Eric Xing, Zhiqiang Shen

We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for effective dataset condensation.

Bilevel Optimization Dataset Condensation +1

Identification of Nonlinear Latent Hierarchical Models

no code implementations13 Jun 2023 Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang

In this work, we investigate the identification problem for nonlinear latent hierarchical causal models in which observed variables are generated by a set of causally related latent variables, and some latent variables may not have observed children.

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

1 code implementation13 Jun 2023 Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, Zhiqiang Shen

We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks.

Domain Generalization Few-Shot Learning +1

Weakly Supervised 3D Open-vocabulary Segmentation

1 code implementation23 May 2023 Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu, Yingchen Yu, Abdulmotaleb El Saddik, Christian Theobalt, Eric Xing, Shijian Lu

Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research.

Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming

1 code implementation5 May 2023 HANLIN ZHANG, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing

We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning.

Logical Reasoning

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

1 code implementation CVPR 2023 Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing

In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively.

3D Semantic Segmentation Autonomous Driving

KD-DLGAN: Data Limited Image Generation via Knowledge Distillation

no code implementations CVPR 2023 Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu1, Eric Xing

The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models.

Image Generation Knowledge Distillation

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

1 code implementation CVPR 2023 Kunhao Liu, Fangneng Zhan, YiWen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing

In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.

Style Transfer

Memory-adaptive Depth-wise Heterogenous Federated Learning

1 code implementation8 Mar 2023 Kai Zhang, Yutong Dai, Hongyi Wang, Eric Xing, Xun Chen, Lichao Sun

Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data.

Federated Learning

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

1 code implementation16 Dec 2022 HANLIN ZHANG, Yi-Fan Zhang, Li Erran Li, Eric Xing

Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations (or ``chain-of-thought'' (CoT)) for in-context learning.

MixMask: Revisiting Masking Strategy for Siamese ConvNets

no code implementations20 Oct 2022 Kirill Vishniakov, Eric Xing, Zhiqiang Shen

These include (I) the inability to drop uninformative masked regions in ConvNets as they process data continuously, resulting in low training efficiency compared to ViT models; and (II) the mismatch between erase-based masking and the contrastive-based objective in Siamese ConvNets, which differs from the MIM approach.

object-detection Object Detection +1

AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness

1 code implementation13 Oct 2022 Dacheng Li, Hongyi Wang, Eric Xing, Hao Zhang

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks.

Betty: An Automatic Differentiation Library for Multilevel Optimization

1 code implementation5 Jul 2022 Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing

Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement learning.

Hyperparameter Optimization Meta-Learning +1

Rare Gems: Finding Lottery Tickets at Initialization

1 code implementation24 Feb 2022 Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos

Frankle & Carbin conjecture that we can avoid this by training "lottery tickets", i. e., special sparse subnetworks found at initialization, that can be trained to high accuracy.

Stochastic Neural Networks with Infinite Width are Deterministic

no code implementations30 Jan 2022 Liu Ziyin, HANLIN ZHANG, Xiangming Meng, Yuting Lu, Eric Xing, Masahito Ueda

This work theoretically studies stochastic neural networks, a main type of neural network in use.

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

1 code implementation CVPR 2022 Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework.

Multimodal Image Synthesis and Editing: The Generative AI Era

2 code implementations27 Dec 2021 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing

With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.

Image Generation

Data-Free Neural Architecture Search via Recursive Label Calibration

no code implementations3 Dec 2021 Zechun Liu, Zhiqiang Shen, Yun Long, Eric Xing, Kwang-Ting Cheng, Chas Leichner

We identify that the NAS task requires the synthesized data (we target at image domain here) with enough semantics, diversity, and a minimal domain gap from the natural images.

Neural Architecture Search

A Fast Knowledge Distillation Framework for Visual Recognition

2 code implementations2 Dec 2021 Zhiqiang Shen, Eric Xing

In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, while training faster than ReLabel since no post-processes such as RoI align and softmax operations are used.

Image Classification Knowledge Distillation +2

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

1 code implementation CVPR 2022 Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric Xing, Zhiqiang Shen

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i. e., uniform strategy, due to its superior representational capacity.


Learning from Mistakes -- A Framework for Neural Architecture Search

1 code implementation11 Nov 2021 Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric Xing, Pengtao Xie

We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision.

BIG-bench Machine Learning Neural Architecture Search

Sliced Recursive Transformer

1 code implementation9 Nov 2021 Zhiqiang Shen, Zechun Liu, Eric Xing

The proposed weight sharing mechanism by sliced recursion structure allows us to build a transformer with more than 100 or even 1000 shared layers with ease while keeping a compact size (13~15M), to avoid optimization difficulties when the model is too large.

Image Classification

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

no code implementations5 Nov 2021 Haohan Wang, Bryon Aragam, Eric Xing

Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS.

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

1 code implementation5 Nov 2021 Haohan Wang, Zeyi Huang, HANLIN ZHANG, Yong Jae Lee, Eric Xing

Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.

BIG-bench Machine Learning

Multi-task Learning of Order-Consistent Causal Graphs

no code implementations NeurIPS 2021 Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.

Multi-Task Learning

Text Generation with Efficient (Soft) $Q$-Learning

no code implementations29 Sep 2021 Han Guo, Bowen Tan, Zhengzhong Liu, Eric Xing, Zhiting Hu

We apply the approach to a wide range of text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.

Q-Learning Reinforcement Learning (RL) +1

Multi-modal Self-supervised Pre-training for Large-scale Genome Data

no code implementations NeurIPS Workshop AI4Scien 2021 Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Yanyan Lan, Zhiqiang Shen, Eric Xing

In this work, we propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT.

Towards Visual Question Answering on Pathology Images

1 code implementation ACL 2021 Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images.

Decision Making Question Answering +1

Prototypical Graph Contrastive Learning

1 code implementation17 Jun 2021 Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang

However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.

Clustering Contrastive Learning +1

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

1 code implementation20 May 2021 Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing

In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.

Binary Classification Classification +1

On Data Efficiency of Meta-learning

no code implementations30 Jan 2021 Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar

Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks.

Meta-Learning Personalized Federated Learning

Learning Robust Models by Countering Spurious Correlations

no code implementations1 Jan 2021 Haohan Wang, Zeyi Huang, Eric Xing

In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.

Domain Adaptation

On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

no code implementations1 Jan 2021 Haohan Wang, Zeyi Huang, Xindi Wu, Eric Xing

Data augmentation is one of the most popular techniques for improving the robustness of neural networks.

Data Augmentation

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

1 code implementation ICLR 2021 Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

Weakly Supervised Classification

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

1 code implementation ICLR 2021 Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.

Federated Learning

Pathological Visual Question Answering

no code implementations6 Oct 2020 Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset.

Question Answering Self-Supervised Learning +1

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

no code implementations28 Sep 2020 Ben Lengerich, Eric Xing, Rich Caruana

Conversely, the probability of an interaction of $k$ variables surviving Dropout at rate $p$ is $\mathcal{O}((1-p)^k)$.

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

1 code implementation17 Jun 2020 Xingyi Yang, Nandiraju Gireesh, Eric Xing, Pengtao Xie

To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students.

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

no code implementations ACL 2019 Baoyu Jing, Zeya Wang, Eric Xing

In this work, we propose a novel framework that exploits the structure information between and within report sections for generating CXR imaging reports.


PathVQA: 30000+ Questions for Medical Visual Question Answering

6 code implementations7 Mar 2020 Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer.

Medical Visual Question Answering Question Answering +1

Methods for comparing uncertainty quantifications for material property predictions

1 code implementation20 Dec 2019 Kevin Tran, Willie Neiswanger, Junwoong Yoon, Eric Xing, Zachary W. Ulissi

These uncertainty estimates are instrumental for determining which materials to screen next, but there is not yet a standard procedure for judging the quality of such uncertainty estimates objectively.

Materials Science Computational Physics

Generalized Zero-shot ICD Coding

no code implementations28 Sep 2019 Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing

The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses.

General Classification Generalized Zero-Shot Learning +3

Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach

no code implementations25 Sep 2019 Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.

BIG-bench Machine Learning Interpretable Machine Learning

Efficient Exploration via State Marginal Matching

1 code implementation12 Jun 2019 Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov

The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective.

Efficient Exploration Unsupervised Reinforcement Learning

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

no code implementations31 May 2019 Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality.

BIG-bench Machine Learning Interpretable Machine Learning

Explaining a black-box using Deep Variational Information Bottleneck Approach

3 code implementations19 Feb 2019 Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.

BIG-bench Machine Learning Interpretable Machine Learning

Regularizing Black-box Models for Improved Interpretability

1 code implementation NeurIPS 2020 Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable.

BIG-bench Machine Learning Interpretable Machine Learning

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

1 code implementation31 Jan 2019 Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.

Bayesian Optimization Gaussian Processes +1

Data-to-Text Generation with Style Imitation

1 code implementation Findings of the Association for Computational Linguistics 2020 Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.

Data-to-Text Generation Style Transfer

Text Infilling

1 code implementation1 Jan 2019 Wanrong Zhu, Zhiting Hu, Eric Xing

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting.

Retrieval Text Infilling

Connecting the Dots Between MLE and RL for Sequence Prediction

no code implementations24 Nov 2018 Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing

Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.

Imitation Learning Machine Translation +2

Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

no code implementations20 Nov 2018 Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs).

Extractive Summarization

On the Complexity of Exploration in Goal-Driven Navigation

no code implementations16 Nov 2018 Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, Eric Xing

Next, we propose to measure the complexity of each environment by constructing dependency graphs between the goals and analytically computing \emph{hitting times} of a random walk in the graph.


AutoLoss: Learning Discrete Schedules for Alternate Optimization

1 code implementation4 Oct 2018 Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

Image Generation Machine Translation +4

AutoLoss: Learning Discrete Schedule for Alternate Optimization

no code implementations ICLR 2019 Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

Image Generation Machine Translation +3

Missing Value Imputation Based on Deep Generative Models

no code implementations5 Aug 2018 Hongbao Zhang, Pengtao Xie, Eric Xing

In this paper, we propose a probabilistic framework based on deep generative models for MVI.


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

no code implementations ECCV 2018 Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing

To our knowledge, this is the first successful case of the learned driving policy through reinforcement learning in the high-fidelity simulator, which performs better-than supervised imitation learning.

Imitation Learning reinforcement-learning +1

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

no code implementations10 Jul 2018 Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric Xing

Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models.

Zero-Shot Learning

DiCE: The Infinitely Differentiable Monte Carlo Estimator

1 code implementation ICML 2018 Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson

Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.


Nonoverlap-Promoting Variable Selection

no code implementations ICML 2018 Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing

Variable selection is a classic problem in machine learning (ML), widely used to find important explanatory factors, and improve generalization performance and interpretability of ML models.

Variable Selection

A Neural Architecture for Automated ICD Coding

no code implementations ACL 2018 Pengtao Xie, Eric Xing

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases.

Gated Path Planning Networks

3 code implementations ICML 2018 Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov

Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture.

Self-Training for Jointly Learning to Ask and Answer Questions

no code implementations NAACL 2018 Mrinmaya Sachan, Eric Xing

The two tasks of question answering and question generation are usually tackled separately in the NLP literature.

Data Augmentation Question Answering +2

Dynamic-structured Semantic Propagation Network

no code implementations CVPR 2018 Xiaodan Liang, Hongfei Zhou, Eric Xing

Moreoever, we demonstrate a universal segmentation model that is jointly trained on diverse datasets can surpass the performance of the common fine-tuning scheme for exploiting multiple domain knowledge.

Universal Segmentation

Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms

no code implementations12 Feb 2018 Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.

Semantic Segmentation

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

1 code implementation NeurIPS 2018 Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

Bayesian Optimisation BIG-bench Machine Learning +2

Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

no code implementations ECCV 2018 Luona Yang, Xiaodan Liang, Tairui Wang, Eric Xing

In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale.

Autonomous Driving

On the Automatic Generation of Medical Imaging Reports

4 code implementations ACL 2018 Baoyu Jing, Pengtao Xie, Eric Xing

To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs.

Medical Report Generation Multi-Task Learning

Predicting Discharge Medications at Admission Time Based on Deep Learning

no code implementations4 Nov 2017 Yuan Yang, Pengtao Xie, Xin Gao, Carol Cheng, Christy Li, Hongbao Zhang, Eric Xing

Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay.

Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks

no code implementations SEMEVAL 2017 Mrinmaya Sachan, Eric Xing

As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks.

Question Answering

Nonparametric Variational Auto-encoders for Hierarchical Representation Learning

no code implementations ICCV 2017 Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing

In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.

Clustering Representation Learning +1

Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling

no code implementations25 Jul 2016 Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye

Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.

Post-Inference Prior Swapping

no code implementations ICML 2017 Willie Neiswanger, Eric Xing

However, we demonstrate that IS will fail for many choices of the target prior, depending on its parametric form and similarity to the false prior.

Harnessing Deep Neural Networks with Logic Rules

2 code implementations ACL 2016 Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.

named-entity-recognition Named Entity Recognition +2

Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization

no code implementations23 Dec 2015 Pengtao Xie, Yuntian Deng, Eric Xing

On two popular latent variable models --- restricted Boltzmann machine and distance metric learning, we demonstrate that MAR can effectively capture long-tail patterns, reduce model complexity without sacrificing expressivity and improve interpretability.

Metric Learning

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

no code implementations19 Dec 2015 Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks.

Object Recognition

Distributed Machine Learning via Sufficient Factor Broadcasting

no code implementations26 Nov 2015 Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.

BIG-bench Machine Learning

On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization

no code implementations23 Nov 2015 Pengtao Xie, Yuntian Deng, Eric Xing

Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several issues involved in latent variable modeling: (1) how to capture long-tail patterns underlying data; (2) how to reduce model complexity without sacrificing expressivity; (3) how to improve the interpretability of learned patterns.

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

no code implementations13 Nov 2015 William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.

Gaussian Processes

Embarrassingly Parallel Variational Inference in Nonconjugate Models

no code implementations14 Oct 2015 Willie Neiswanger, Chong Wang, Eric Xing

We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines.

Variational Inference

Cauchy Principal Component Analysis

no code implementations19 Dec 2014 Pengtao Xie, Eric Xing

In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise.

Large Scale Distributed Distance Metric Learning

no code implementations18 Dec 2014 Pengtao Xie, Eric Xing

In large scale machine learning and data mining problems with high feature dimensionality, the Euclidean distance between data points can be uninformative, and Distance Metric Learning (DML) is often desired to learn a proper similarity measure (using side information such as example data pairs being similar or dissimilar).

Metric Learning

Fast Function to Function Regression

no code implementations27 Oct 2014 Junier Oliva, Willie Neiswanger, Barnabas Poczos, Eric Xing, Jeff Schneider

Function to function regression (FFR) covers a large range of interesting applications including time-series prediction problems, and also more general tasks like studying a mapping between two separate types of distributions.

regression Time Series +1

Distributed Machine Learning via Sufficient Factor Broadcasting

no code implementations19 Sep 2014 Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.

BIG-bench Machine Learning

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

no code implementations16 Jan 2014 Le Song, Han Liu, Ankur Parikh, Eric Xing

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.

Asymptotically Exact, Embarrassingly Parallel MCMC

no code implementations19 Nov 2013 Willie Neiswanger, Chong Wang, Eric Xing

This embarrassingly parallel algorithm allows each machine to act independently on a subset of the data (without communication) until the final combination stage.

Fast Distribution To Real Regression

no code implementations10 Nov 2013 Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

We study the problem of distribution to real-value regression, where one aims to regress a mapping $f$ that takes in a distribution input covariate $P\in \mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and outputs a real-valued response $Y=f(P) + \epsilon$.


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