Caiming Xiong

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Microsoft ∙ Salesforce ∙ University at Buffalo

Director of Research, AI/ML at Salesforce

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

We present CoSQL, a corpus for building cross-domain, general-purpose da...
09/11/2019 ∙ by Tao Yu, et al. ∙ 39 ∙ share

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Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning

We introduce a parameterization method called Neural Bayes which allows ...
02/20/2020 ∙ by Devansh Arpit, et al. ∙ 36 ∙ share

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ERASER: A Benchmark to Evaluate Rationalized NLP Models

State-of-the-art models in NLP are now predominantly based on deep neura...
11/08/2019 ∙ by Jay DeYoung, et al. ∙ 35 ∙ share

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Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as ...
11/18/2019 ∙ by Tong Che, et al. ∙ 26 ∙ share

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WSLLN: Weakly Supervised Natural Language Localization Networks

We propose weakly supervised language localization networks (WSLLN) to d...
08/31/2019 ∙ by Mingfei Gao, et al. ∙ 24 ∙ share

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Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking

Dialog State Tracking (DST) is a core component in task-oriented dialog ...
10/08/2019 ∙ by Jian-Guo Zhang, et al. ∙ 23 ∙ share

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Global Capacity Measures for Deep ReLU Networks via Path Sampling

Classical results on the statistical complexity of linear models have co...
10/22/2019 ∙ by Ryan Theisen, et al. ∙ 23 ∙ share

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Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

We propose Taylorized training as an initiative towards better understan...
02/10/2020 ∙ by Yu Bai, et al. ∙ 20 ∙ share

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Global-to-local Memory Pointer Networks for Task-Oriented Dialogue

End-to-end task-oriented dialogue is challenging since knowledge bases a...
01/15/2019 ∙ by Chien-Sheng Wu, et al. ∙ 18 ∙ share

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Differentially Private Deep Learning with Smooth Sensitivity

Ensuring the privacy of sensitive data used to train modern machine lear...
03/01/2020 ∙ by Lichao Sun, et al. ∙ 14 ∙ share

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Identifying Generalization Properties in Neural Networks

While it has not yet been proven, empirical evidence suggests that model...
09/19/2018 ∙ by Huan Wang, et al. ∙ 12 ∙ share

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Learning from Noisy Anchors for One-stage Object Detection

State-of-the-art object detectors rely on regressing and classifying an ...
12/11/2019 ∙ by Hengduo Li, et al. ∙ 12 ∙ share

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Entropy Penalty: Towards Generalization Beyond the IID Assumption

It has been shown that instead of learning actual object features, deep ...
10/01/2019 ∙ by Devansh Arpit, et al. ∙ 11 ∙ share

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Proposal Learning for Semi-Supervised Object Detection

In this paper, we focus on semi-supervised object detection to boost acc...
01/15/2020 ∙ by Peng Tang, et al. ∙ 11 ∙ share

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Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Acquiring abilities in the absence of a task-oriented reward function is...
02/10/2020 ∙ by Victor Campos, et al. ∙ 11 ∙ share

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LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition

This paper presents LiteEval, a simple yet effective coarse-to-fine fram...
12/03/2019 ∙ by Zuxuan Wu, et al. ∙ 9 ∙ share

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The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation

As deep learning continues to make progress for challenging perception t...
03/05/2019 ∙ by Chih-Yao Ma, et al. ∙ 8 ∙ share

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On the Generalization Gap in Reparameterizable Reinforcement Learning

Understanding generalization in reinforcement learning (RL) is a signifi...
05/29/2019 ∙ by Huan Wang, et al. ∙ 8 ∙ share

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Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

While using shaped rewards can be beneficial when solving sparse reward ...
11/04/2019 ∙ by Alexander Trott, et al. ∙ 8 ∙ share

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Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

End-to-end neural models have made significant progress in question answ...
01/03/2019 ∙ by Victor Zhong, et al. ∙ 6 ∙ share

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Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting

Addressing catastrophic forgetting is one of the key challenges in conti...
03/31/2019 ∙ by Xilai Li, et al. ∙ 6 ∙ share

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Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

In many real-world scenarios, an autonomous agent often encounters vario...
07/01/2019 ∙ by Wenling Shang, et al. ∙ 5 ∙ share

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AdaFrame: Adaptive Frame Selection for Fast Video Recognition

We present AdaFrame, a framework that adaptively selects relevant frames...
11/29/2018 ∙ by Zuxuan Wu, et al. ∙ 4 ∙ share

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Competitive Experience Replay

Deep learning has achieved remarkable successes in solving challenging r...
02/01/2019 ∙ by Hao Liu, et al. ∙ 4 ∙ share

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Augmented Cyclic Adversarial Learning for Domain Adaptation

Training a model to perform a task typically requires a large amount of ...
07/01/2018 ∙ by Ehsan Hosseini-Asl, et al. ∙ 2 ∙ share

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Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

The Vision-and-Language Navigation (VLN) task entails an agent following...
01/10/2019 ∙ by Chih-Yao Ma, et al. ∙ 2 ∙ share

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StartNet: Online Detection of Action Start in Untrimmed Videos

We propose StartNet to address Online Detection of Action Start (ODAS) w...
03/23/2019 ∙ by Mingfei Gao, et al. ∙ 2 ∙ share

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Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems

Over-dependence on domain ontology and lack of knowledge sharing across ...
05/21/2019 ∙ by Chien-Sheng Wu, et al. ∙ 2 ∙ share

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XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering

While natural language processing systems often focus on a single langua...
05/27/2019 ∙ by Jasdeep Singh, et al. ∙ 2 ∙ share

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Towards Noise-resistant Object Detection with Noisy Annotations

Training deep object detectors requires significant amount of human-anno...
03/03/2020 ∙ by Junnan Li, et al. ∙ 2 ∙ share

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Pointer Sentinel Mixture Models

Recent neural network sequence models with softmax classifiers have achi...
09/26/2016 ∙ by Stephen Merity, et al. ∙ 0 ∙ share

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Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely adopt...
12/06/2016 ∙ by Jiasen Lu, et al. ∙ 0 ∙ share

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Recognizing Car Fluents from Video

Physical fluents, a term originally used by Newton [40], refers to time-...
03/26/2016 ∙ by Bo Li, et al. ∙ 0 ∙ share

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Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies

Metric learning is a key problem for many data mining and machine learni...
02/23/2014 ∙ by David M. Johnson, et al. ∙ 0 ∙ share

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Compositional Structure Learning for Action Understanding

The focus of the action understanding literature has predominately been ...
10/21/2014 ∙ by Ran Xu, et al. ∙ 0 ∙ share

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Random Forests for Metric Learning with Implicit Pairwise Position Dependence

Metric learning makes it plausible to learn distances for complex distri...
01/03/2012 ∙ by Caiming Xiong, et al. ∙ 0 ∙ share

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Active Clustering with Model-Based Uncertainty Reduction

Semi-supervised clustering seeks to augment traditional clustering metho...
02/07/2014 ∙ by Caiming Xiong, et al. ∙ 0 ∙ share

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Non-Autoregressive Neural Machine Translation

Existing approaches to neural machine translation condition each output ...
11/07/2017 ∙ by Jiatao Gu, et al. ∙ 0 ∙ share

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DCN+: Mixed Objective and Deep Residual Coattention for Question Answering

Traditional models for question answering optimize using cross entropy l...
10/31/2017 ∙ by Caiming Xiong, et al. ∙ 0 ∙ share

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Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

A significant amount of the world's knowledge is stored in relational da...
08/31/2017 ∙ by Victor Zhong, et al. ∙ 0 ∙ share

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Learned in Translation: Contextualized Word Vectors

Computer vision has benefited from initializing multiple deep layers wit...
08/01/2017 ∙ by Bryan McCann, et al. ∙ 0 ∙ share

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A Deep Reinforced Model for Abstractive Summarization

Attentional, RNN-based encoder-decoder models for abstractive summarizat...
05/11/2017 ∙ by Romain Paulus, et al. ∙ 0 ∙ share

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A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs

LSTMs have become a basic building block for many deep NLP models. In re...
11/16/2016 ∙ by Shayne Longpre, et al. ∙ 0 ∙ share

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Dynamic Coattention Networks For Question Answering

Several deep learning models have been proposed for question answering. ...
11/05/2016 ∙ by Caiming Xiong, et al. ∙ 0 ∙ share

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A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Transfer and multi-task learning have traditionally focused on either a ...
11/05/2016 ∙ by Kazuma Hashimoto, et al. ∙ 0 ∙ share

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Quasi-Recurrent Neural Networks

Recurrent neural networks are a powerful tool for modeling sequential da...
11/05/2016 ∙ by James Bradbury, et al. ∙ 0 ∙ share

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Dynamic Memory Networks for Visual and Textual Question Answering

Neural network architectures with memory and attention mechanisms exhibi...
03/04/2016 ∙ by Caiming Xiong, et al. ∙ 0 ∙ share

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Block-diagonal Hessian-free Optimization for Training Neural Networks

Second-order methods for neural network optimization have several advant...
12/20/2017 ∙ by Huishuai Zhang, et al. ∙ 0 ∙ share

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Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning

Learning policies for complex tasks that require multiple different skil...
12/20/2017 ∙ by Tianmin Shu, et al. ∙ 0 ∙ share

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Interpretable Counting for Visual Question Answering

Questions that require counting a variety of objects in images remain a ...
12/23/2017 ∙ by Alexander Trott, et al. ∙ 0 ∙ share

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Caiming Xiong

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