Search Results for author: Bo Dai

Found 87 papers, 38 papers with code

Combiner: Full Attention Transformer with Sparse Computation Cost

no code implementations12 Jul 2021 Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai

However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.

Image Generation Language Modelling

Safe Exploration by Solving Early Terminated MDP

no code implementations9 Jul 2021 Hao Sun, Ziping Xu, Meng Fang, Zhenghao Peng, Jiadong Guo, Bo Dai, Bolei Zhou

Safe exploration is crucial for the real-world application of reinforcement learning (RL).

Safe Exploration

On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data

no code implementations18 Jun 2021 Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvari

We study the fundamental question of the sample complexity of learning a good policy in finite Markov decision processes (MDPs) when the data available for learning is obtained by following a logging policy that must be chosen without knowledge of the underlying MDP.

Optimization Variance: Exploring Generalization Properties of DNNs

1 code implementation3 Jun 2021 Xiao Zhang, Dongrui Wu, Haoyi Xiong, Bo Dai

Unlike the conventional wisdom in statistical learning theory, the test error of a deep neural network (DNN) often demonstrates double descent: as the model complexity increases, it first follows a classical U-shaped curve and then shows a second descent.

Learning Theory

Scene-aware Generative Network for Human Motion Synthesis

no code implementations CVPR 2021 Jingbo Wang, Sijie Yan, Bo Dai, Dahua Lin

We revisit human motion synthesis, a task useful in various real world applications, in this paper.

motion synthesis

Leveraging Non-uniformity in First-order Non-convex Optimization

no code implementations13 May 2021 Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans

Classical global convergence results for first-order methods rely on uniform smoothness and the \L{}ojasiewicz inequality.

Revisiting Skeleton-based Action Recognition

1 code implementation28 Apr 2021 Haodong Duan, Yue Zhao, Kai Chen, Dian Shao, Dahua Lin, Bo Dai

In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.

Action Recognition Group Activity Recognition +2

Unsupervised 3D Shape Completion through GAN Inversion

no code implementations CVPR 2021 Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, Chen Change Loy

In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.

GAN inversion

Visually Informed Binaural Audio Generation without Binaural Audios

no code implementations CVPR 2021 Xudong Xu, Hang Zhou, Ziwei Liu, Bo Dai, Xiaogang Wang, Dahua Lin

Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings.

Audio Generation

On the Optimality of Batch Policy Optimization Algorithms

no code implementations6 Apr 2021 Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvari, Dale Schuurmans

First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis.

Value prediction

Nearly Horizon-Free Offline Reinforcement Learning

no code implementations25 Mar 2021 Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi

Given the collected $N$ episodes data with minimum cumulative reaching probability $d_m$, we obtain the first set of nearly $H$-free sample complexity bounds for evaluation and planning using the empirical MDPs: 1. For the offline evaluation, we obtain an $\tilde{O}\left(\sqrt{\frac{1}{Nd_m}} \right)$ error rate, which matches the lower bound and does not have additional dependency on $\poly\left(S, A\right)$ in higher-order term, that is different from previous works~\citep{yin2020near, yin2020asymptotically}.

DeepStyle: User Style Embedding for Authorship Attribution of Short Texts

no code implementations14 Mar 2021 Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim, Bo Dai

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications.

Text Classification

Off-Policy Imitation Learning from Observations

no code implementations NeurIPS 2020 Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.

Imitation Learning

Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

1 code implementation20 Feb 2021 Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei Wei

An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.

Model-based Reinforcement Learning Text Generation

Self-Supervised Continuous Control without Policy Gradient

no code implementations1 Jan 2021 Hao Sun, Ziping Xu, Meng Fang, Yuhang Song, Jiechao Xiong, Bo Dai, Zhengyou Zhang, Bolei Zhou

Despite the remarkable progress made by the policy gradient algorithms in reinforcement learning (RL), sub-optimal policies usually result from the local exploration property of the policy gradient update.

Continuous Control Policy Gradient Methods +1

Slow Control System for PandaX-III experiment

no code implementations24 Dec 2020 Xiyu Yan, Xun Chen, Yu Chen, Bo Dai, Heng Lin, Tao Li, Ke Han, Kaixiang Ni, Fusang Wang, Shaobo Wang, Qibin Zheng, Xinning Zeng

The PandaX-III experiment uses high pressure gaseous time projection chamber to search for the neutrinoless double beta decay of $^{136}$Xe.

Anomaly Detection High Energy Physics - Experiment Instrumentation and Detectors

Focal Frequency Loss for Image Reconstruction and Synthesis

1 code implementation23 Dec 2020 Liming Jiang, Bo Dai, Wayne Wu, Chen Change Loy

In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further.

 Ranked #1 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-pixel Accuracy metric)

Image Reconstruction Image-to-Image Translation

Offline Policy Selection under Uncertainty

1 code implementation12 Dec 2020 Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans

More importantly, we show how the belief distribution estimated by BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric, and we empirically demonstrate that this selection procedure significantly outperforms existing approaches, such as ranking policies according to mean or high-confidence lower bound value estimates.

Escaping the Gravitational Pull of Softmax

no code implementations NeurIPS 2020 Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvari, Dale Schuurmans

Both findings are based on an analysis of convergence rates using the Non-uniform \L{}ojasiewicz (N\L{}) inequalities.

Differentiable Top-k with Optimal Transport

no code implementations NeurIPS 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.

Information Retrieval

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach

no code implementations NeurIPS 2020 Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang

We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration

no code implementations NeurIPS 2020 Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans

In this paper we propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data, where parameter gradients are estimated using a learned sampler that mimics local search.

Language Modelling

Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs

1 code implementation ICLR 2021 Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo

Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner.

3D Shape Reconstruction

Named Entity Recognition for Social Media Texts with Semantic Augmentation

1 code implementation EMNLP 2020 Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai

In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.

Chinese Named Entity Recognition NER +1

CoinDICE: Off-Policy Confidence Interval Estimation

no code implementations NeurIPS 2020 Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans

We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given only access to a static experience dataset collected by unknown behavior policies.

Small Towers Make Big Differences

no code implementations13 Aug 2020 Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi

A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.

Multi-Task Learning

Energy-based View of Retrosynthesis

no code implementations14 Jul 2020 Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.

Drug Discovery Single-step retrosynthesis

Off-Policy Evaluation via the Regularized Lagrangian

no code implementations NeurIPS 2020 Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

The recently proposed distribution correction estimation (DICE) family of estimators has advanced the state of the art in off-policy evaluation from behavior-agnostic data.

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

no code implementations2 Jul 2020 Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar

We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.

Unsupervised Landmark Learning from Unpaired Data

1 code implementation29 Jun 2020 Yinghao Xu, Ceyuan Yang, Ziwei Liu, Bo Dai, Bolei Zhou

Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses.

Video Representation Learning with Visual Tempo Consistency

1 code implementation28 Jun 2020 Ceyuan Yang, Yinghao Xu, Bo Dai, Bolei Zhou

Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition.

Action Anticipation Action Detection +3

Scalable Deep Generative Modeling for Sparse Graphs

1 code implementation ICML 2020 Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans

Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$.

Graph Generation

Zeroth-Order Supervised Policy Improvement

no code implementations11 Jun 2020 Hao Sun, Ziping Xu, Yuhang Song, Meng Fang, Jiechao Xiong, Bo Dai, Bolei Zhou

However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limited sample efficiency.

Continuous Control Policy Gradient Methods +1

Novel Policy Seeking with Constrained Optimization

no code implementations21 May 2020 Hao Sun, Zhenghao Peng, Bo Dai, Jian Guo, Dahua Lin, Bolei Zhou

In problem-solving, we humans can come up with multiple novel solutions to the same problem.

Intra- and Inter-Action Understanding via Temporal Action Parsing

no code implementations CVPR 2020 Dian Shao, Yue Zhao, Bo Dai, Dahua Lin

Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features.

Action Parsing Action Recognition +1

Evolutionary Stochastic Policy Distillation

2 code implementations27 Apr 2020 Hao Sun, Xinyu Pan, Bo Dai, Dahua Lin, Bolei Zhou

Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals.

Temporal Pyramid Network for Action Recognition

3 code implementations CVPR 2020 Ceyuan Yang, Yinghao Xu, Jianping Shi, Bo Dai, Bolei Zhou

Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle.

Action Recognition

Self-Supervised Scene De-occlusion

no code implementations CVPR 2020 Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, Chen Change Loy

This is achieved via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner.

Image Manipulation Scene Understanding

Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations

no code implementations1 Apr 2020 Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.

Continuous Control Decision Making +1

Energy-Based Processes for Exchangeable Data

1 code implementation ICML 2020 Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Recently there has been growing interest in modeling sets with exchangeability such as point clouds.

Denoising Point Cloud Generation

Batch Stationary Distribution Estimation

1 code implementation ICML 2020 Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans

We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions.

GenDICE: Generalized Offline Estimation of Stationary Values

2 code implementations ICLR 2020 Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans

An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain.

Differentiable Top-k Operator with Optimal Transport

no code implementations16 Feb 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval

Real or Not Real, that is the Question

2 code implementations ICLR 2020 Yuanbo Xiangli, Yubin Deng, Bo Dai, Chen Change Loy, Dahua Lin

While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles.

Reinforcement Learning via Fenchel-Rockafellar Duality

1 code implementation7 Jan 2020 Ofir Nachum, Bo Dai

We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality.

AlgaeDICE: Policy Gradient from Arbitrary Experience

no code implementations4 Dec 2019 Ofir Nachum, Bo Dai, Ilya Kostrikov, Yin-Lam Chow, Lihong Li, Dale Schuurmans

In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility.

Overcoming Catastrophic Forgetting by Generative Regularization

no code implementations3 Dec 2019 Patrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai

In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework.

Bayesian Inference Continual Learning

Energy-Inspired Models: Learning with Sampler-Induced Distributions

1 code implementation NeurIPS 2019 Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model.

Variational Inference

Recursive Visual Sound Separation Using Minus-Plus Net

no code implementations ICCV 2019 Xudong Xu, Bo Dai, Dahua Lin

Sounds provide rich semantics, complementary to visual data, for many tasks.

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

2 code implementations NeurIPS 2019 Ofir Nachum, Yin-Lam Chow, Bo Dai, Lihong Li

In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset.

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification

no code implementations NeurIPS 2018 Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru

We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure.

Classification General Classification +1

Exponential Family Estimation via Adversarial Dynamics Embedding

1 code implementation NeurIPS 2019 Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks.

Feature Intertwiner for Object Detection

2 code implementations ICLR 2019 Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang

We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space.

Object Detection

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

1 code implementation ICLR 2020 Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.

Meta Architecture Search

1 code implementation NeurIPS 2019 Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.

Bayesian Inference Few-Shot Learning +1

Predictive Approximate Bayesian Computation via Saddle Points

no code implementations NeurIPS 2018 Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.

Bayesian Inference

Coupled Variational Bayes via Optimization Embedding

1 code implementation NeurIPS 2018 Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.

Variational Inference

Kernel Exponential Family Estimation via Doubly Dual Embedding

1 code implementation6 Nov 2018 Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space.

Learning to Defend by Learning to Attack

no code implementations3 Nov 2018 Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao

Adversarial training provides a principled approach for training robust neural networks.

Adversarial Attack Adversarial Defense +2

A Neural Compositional Paradigm for Image Captioning

1 code implementation NeurIPS 2018 Bo Dai, Sanja Fidler, Dahua Lin

Mainstream captioning models often follow a sequential structure to generate captions, leading to issues such as introduction of irrelevant semantics, lack of diversity in the generated captions, and inadequate generalization performance.

Image Captioning

Neural Network Encapsulation

2 code implementations ECCV 2018 Hongyang Li, Xiaoyang Guo, Bo Dai, Wanli Ouyang, Xiaogang Wang

Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers.

Rethinking the Form of Latent States in Image Captioning

no code implementations ECCV 2018 Bo Dai, Deming Ye, Dahua Lin

Taking advantage of this, we visually reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.

Image Captioning

Move Forward and Tell: A Progressive Generator of Video Descriptions

no code implementations ECCV 2018 Yilei Xiong, Bo Dai, Dahua Lin

We present an efficient framework that can generate a coherent paragraph to describe a given video.

Video Captioning

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

no code implementations22 Jul 2018 Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha

Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.

Learning Steady-States of Iterative Algorithms over Graphs

no code implementations ICML 2018 Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song

Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.

Learning towards Minimum Hyperspherical Energy

4 code implementations NeurIPS 2018 Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.

Decoupled Networks

1 code implementation CVPR 2018 Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

no code implementations ICML 2018 Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.


Boosting the Actor with Dual Critic

no code implementations ICLR 2018 Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song

This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.

Deep Hyperspherical Learning

no code implementations NeurIPS 2017 Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song

In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.

Representation Learning

Towards Black-box Iterative Machine Teaching

no code implementations ICML 2018 Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song

We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.

Contrastive Learning for Image Captioning

no code implementations NeurIPS 2017 Bo Dai, Dahua Lin

Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the overall quality of the generated captions.

Contrastive Learning Image Captioning

Iterative Machine Teaching

2 code implementations ICML 2017 Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song

Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.

Towards Diverse and Natural Image Descriptions via a Conditional GAN

1 code implementation ICCV 2017 Bo Dai, Sanja Fidler, Raquel Urtasun, Dahua Lin

Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e. g. those based on RNNs, are often overly rigid and lacking in variability.

Image Captioning

Stochastic Generative Hashing

2 code implementations ICML 2017 Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases.

Learning from Conditional Distributions via Dual Embeddings

no code implementations15 Jul 2016 Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song

In such problems, each sample $x$ itself is associated with a conditional distribution $p(z|x)$ represented by samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links these conditional distributions to target values $y$.

Discriminative Embeddings of Latent Variable Models for Structured Data

2 code implementations17 Mar 2016 Hanjun Dai, Bo Dai, Le Song

Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design.

Latent Variable Models

Provable Bayesian Inference via Particle Mirror Descent

no code implementations9 Jun 2015 Bo Dai, Niao He, Hanjun Dai, Le Song

Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters.

Bayesian Inference Gaussian Processes

Scalable Kernel Methods via Doubly Stochastic Gradients

1 code implementation NeurIPS 2014 Bo Dai, Bo Xie, Niao He, YIngyu Liang, Anant Raj, Maria-Florina Balcan, Le Song

The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems.

Transductive Learning with Multi-class Volume Approximation

no code implementations3 Feb 2014 Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama

Given a hypothesis space, the large volume principle by Vladimir Vapnik prioritizes equivalence classes according to their volume in the hypothesis space.

Nonparametric Estimation of Multi-View Latent Variable Models

no code implementations13 Nov 2013 Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters.

Latent Variable Models

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