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20132024
most citedA Deep Reinforced Model for Abstractive Summarization

1.3k citations · 12.3k across the 104 of their papers we have counts for

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Showing 2018Show all

13 papers · 1 filter

cs.CV2018★ 16 cited

AdaFrame: Adaptive Frame Selection for Fast Video Recognition

Zuxuan Wu, Caiming Xiong, Chih-Yao Ma +2

We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augment…

cs.LG2018★ 67 cited

A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong +1

The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip…

cs.LG2018★ 37 cited

Identifying Generalization Properties in Neural Networks

Huan Wang, Nitish Shirish Keskar, Caiming Xiong +1

While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We conn…

cs.AI2018★ 36 cited

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

Xi Victoria Lin, Richard Socher, Caiming Xiong

Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup,…

cs.CL2018

Improving Abstraction in Text Summarization

Wojciech Kryściński, Romain Paulus, Caiming Xiong +1

Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level…

cs.LG2018★ 44 cited

Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation

Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong +1

Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundan…