Search Results for author: Jie Fu

Found 53 papers, 20 papers with code

Biological Sequence Design with GFlowNets

1 code implementation2 Mar 2022 Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.

Active Learning

MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare

no code implementations29 Oct 2021 Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, Erik Cambria

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.

Pretrained Language Models

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

1 code implementation11 Oct 2021 Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie Fu

In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.

Learning Multi-Objective Curricula for Deep Reinforcement Learning

no code implementations6 Oct 2021 Jikun Kang, Miao Liu, Abhinav Gupta, Chris Pal, Xue Liu, Jie Fu

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).

reinforcement-learning

Unifying Likelihood-free Inference with Black-box Optimization and Beyond

no code implementations ICLR 2022 Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry.

Drug Discovery

Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning

no code implementations8 Sep 2021 Fan Wang, Hao Tian, Haoyi Xiong, Hua Wu, Jie Fu, Yang Cao, Yu Kang, Haifeng Wang

In contrast, biological neural networks (BNNs) can adapt to various new tasks by continually updating their connection weights based on their observations, which is aligned with the paradigm of learning effective learning rules in addition to static parameters, e. g., meta-learning.

Meta-Learning

Pruning Ternary Quantization

no code implementations23 Jul 2021 Dan Liu, Xi Chen, Jie Fu, Chen Ma, Xue Liu

To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.

Image Classification Model Compression +2

Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning

no code implementations13 Jul 2021 Chuanqiang Shan, Huiyun Jiao, Jie Fu

In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to reality application stage.

Few-Shot Domain Adaptation with Polymorphic Transformers

1 code implementation10 Jul 2021 Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh

Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.

Domain Adaptation

Probabilistic Planning with Preferences over Temporal Goals

no code implementations26 Mar 2021 Jie Fu

We present a formal language for specifying qualitative preferences over temporal goals and a preference-based planning method in stochastic systems.

FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

1 code implementation ICCV 2021 Yuwei Cheng, Jiannan Zhu, Mengxin Jiang, Jie Fu, Changsong Pang, Peidong Wang, Kris Sankaran, Olawale Onabola, Yimin Liu, Dianbo Liu, Yoshua Bengio

To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.

Robust Object Detection

Attention-Based Planning with Active Perception

no code implementations30 Nov 2020 Haoxiang Ma, Jie Fu

By switching between different attention modes, the robot actively perceives task-relevant information to reduce the cost of information acquisition and processing, while achieving near-optimal task performance.

Semantic SLAM with Autonomous Object-Level Data Association

no code implementations20 Nov 2020 Zhentian Qian, Kartik Patath, Jie Fu, Jing Xiao

It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM).

Semantic SLAM

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space

2 code implementations EMNLP 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou

In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.

Data Augmentation Machine Reading Comprehension +4

A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception

no code implementations7 Aug 2020 Abhishek N. Kulkarni, Jie Fu

Given qualitative security specifications in formal logic, we show that the solution concepts from hypergames and reactive synthesis in formal methods can be extended to synthesize effective dynamic defense strategy using cyber deception.

CoCon: A Self-Supervised Approach for Controlled Text Generation

1 code implementation ICLR 2021 Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, there is still a lack of more precise control over its content at the word- and phrase-level.

Text Generation

RikiNet: Reading Wikipedia Pages for Natural Question Answering

no code implementations ACL 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Nan Duan

The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.

Natural Language Understanding Question Answering

Role-Wise Data Augmentation for Knowledge Distillation

1 code implementation ICLR 2020 Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong

To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.

Data Augmentation Knowledge Distillation

Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations

1 code implementation12 Apr 2020 Shaohua Li, Xiuchao Sui, Jie Fu, Yong liu, Rick Siow Mong Goh

To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").

Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation

1 code implementation EMNLP 2020 Dayiheng Liu, Yeyun Gong, Jie Fu, Wei Liu, Yu Yan, Bo Shao, Daxin Jiang, Jiancheng Lv, Nan Duan

Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of $<$news article, headline, keyphrase$>$.

Headline generation

Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot

no code implementations13 Jan 2020 Xuan Liu, Renato Gasoto, Cagdas Onal, Jie Fu

Inspired by biological snakes, our control architecture is composed of two key modules: A deep reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns.

Jacobian Adversarially Regularized Networks for Robustness

1 code implementation ICLR 2020 Alvin Chan, Yi Tay, Yew Soon Ong, Jie Fu

Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks.

Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer

no code implementations9 Sep 2019 Jie Fu, Xinran Zhong, Ning li, Ritchell Van Dams, John Lewis, Kyunghyun Sung, Ann C. Raldow, Jing Jin, X. Sharon Qi

The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0. 64, while the one built with DL-based features yielded the mean AUC of 0. 73.

Survival Prediction

Exploring Domain Shift in Extractive Text Summarization

no code implementations30 Aug 2019 Danqing Wang, PengFei Liu, Ming Zhong, Jie Fu, Xipeng Qiu, Xuanjing Huang

Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.

Extractive Text Summarization Meta-Learning

Interactive Machine Comprehension with Information Seeking Agents

1 code implementation ACL 2020 Xingdi Yuan, Jie Fu, Marc-Alexandre Cote, Yi Tay, Christopher Pal, Adam Trischler

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA).

Decision Making Information Retrieval +2

Conditional Computation for Continual Learning

no code implementations16 Jun 2019 Min Lin, Jie Fu, Yoshua Bengio

In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example.

Continual Learning

Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

1 code implementation29 May 2019 Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, Jiancheng Lv

We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer.

Disentanglement Style Transfer +2

Structure Learning for Neural Module Networks

no code implementations WS 2019 Vardaan Pahuja, Jie Fu, Sarath Chandar, Christopher J. Pal

In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned.

Question Answering Visual Question Answering

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

1 code implementation ACL 2019 Dayiheng Liu, Jie Fu, PengFei Liu, Jiancheng Lv

Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios.

Text Infilling

Language Modeling with Graph Temporal Convolutional Networks

no code implementations ICLR 2019 Hongyin Luo, Yichen Li, Jie Fu, James Glass

Recently, there have been some attempts to use non-recurrent neural models for language modeling.

Language Modelling

Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks

no code implementations4 Jan 2019 Xue Geng, Jie Fu, Bin Zhao, Jie Lin, Mohamed M. Sabry Aly, Christopher Pal, Vijay Chandrasekhar

This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.

Quantization

Multi-task Learning over Graph Structures

no code implementations26 Nov 2018 Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung

We present two architectures for multi-task learning with neural sequence models.

General Classification Multi-Task Learning +1

Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

no code implementations5 Oct 2018 Xuan Liu, Jie Fu

Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying temporal logic specifications.

BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

no code implementations21 Jun 2018 Dayiheng Liu, Jie Fu, Qian Qu, Jiancheng Lv

Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc.

Image Captioning Machine Translation +1

Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes

no code implementations27 Feb 2018 Steven Carr, Nils Jansen, Ralf Wimmer, Jie Fu, Ufuk Topcu

The efficient verification of this MC gives quantitative insights into the quality of the inferred human strategy by proving or disproving given system specifications.

Inverse Reinforce Learning with Nonparametric Behavior Clustering

no code implementations15 Dec 2017 Siddharthan Rajasekaran, Jinwei Zhang, Jie Fu

In this paper, we introduce the Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster demonstrations and learn multiple reward functions from demonstrations that may be generated from more than one behaviors.

Environment-Independent Task Specifications via GLTL

no code implementations14 Apr 2017 Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Isbell, Min Wen, James Macglashan

We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent.

reinforcement-learning

Adaptive Bidirectional Backpropagation: Towards Biologically Plausible Error Signal Transmission in Neural Networks

2 code implementations23 Feb 2017 Hongyin Luo, Jie Fu, James Glass

However, it has been argued that this is not biologically plausible because back-propagating error signals with the exact incoming weights are not considered possible in biological neural systems.

Importance sampling-based approximate optimal planning and control

no code implementations16 Dec 2016 Jie Fu

In this paper, we propose a sampling-based planning and optimal control method of nonlinear systems under non-differentiable constraints.

Systems and Control Robotics 70E60

Deep Q-Networks for Accelerating the Training of Deep Neural Networks

no code implementations5 Jun 2016 Jie Fu

With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN.

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

1 code implementation5 Jan 2016 Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, Tat-Seng Chua

The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters.

Integrating active sensing into reactive synthesis with temporal logic constraints under partial observations

no code implementations1 Oct 2014 Jie Fu, Ufuk Topcu

We show that by alternating between the observation-based strategy and the active sensing strategy, under a mild technical assumption of the set of sensors in the system, the given temporal logic specification can be satisfied with probability 1.

Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraints

no code implementations28 Apr 2014 Jie Fu, Ufuk Topcu

We model the interaction between the system and its environment as a Markov decision process (MDP) with initially unknown transition probabilities.

Cannot find the paper you are looking for? You can Submit a new open access paper.