Search Results for author: Qing Sun

Found 16 papers, 6 papers with code

Sampling weights of deep neural networks

2 code implementations29 Jun 2023 Erik Lien Bolager, Iryna Burak, Chinmay Datar, Qing Sun, Felix Dietrich

For Barron functions, we show that the $L^2$-approximation error of sampled shallow networks decreases with the square root of the number of neurons.

Transfer Learning

Multi-lingual Evaluation of Code Generation Models

1 code implementation26 Oct 2022 Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang

Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.

Code Completion Code Translation +1

Exploring Example Influence in Continual Learning

1 code implementation25 Sep 2022 Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan

Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks).

Continual Learning

Learning to Revise References for Faithful Summarization

1 code implementation13 Apr 2022 Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text.

Clinical Knowledge Contrastive Learning +1

Amortized Posterior on Latent Variables in Gaussian Process

no code implementations29 Sep 2021 Qing Sun

Deep neural networks have achieved impressive performance on a variety of domains.

Rethinking Rehearsal in Lifelong Learning: Does An Example Contribute the Plasticity or Stability?

no code implementations29 Sep 2021 Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan

Traditionally, the primary goal of LL is to achieve the trade-off between the Stability (remembering past tasks) and Plasticity (adapting to new tasks).

Multi-Task Learning

Neural Entity Recognition with Gazetteer based Fusion

no code implementations Findings (ACL) 2021 Qing Sun, Parminder Bhatia

Our gazetteer based fusion model is data efficient, achieving +1. 7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4. 7 micro-F1 gains on novel entity mentions never presented during training.

named-entity-recognition Named Entity Recognition +1

An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training

1 code implementation EMNLP 2020 Kristjan Arumae, Qing Sun, Parminder Bhatia

However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.

Clustering Language Modelling +4

Learn to Talk via Proactive Knowledge Transfer

no code implementations23 Aug 2020 Qing Sun, James Cross

In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward.

Knowledge Distillation Machine Translation +2

Proactive Sequence Generator via Knowledge Acquisition

no code implementations25 Sep 2019 Qing Sun, James Cross, Dmitriy Genzel

Sequence-to-sequence models such as transformers, which are now being used in a wide variety of NLP tasks, typically need to have very high capacity in order to perform well.

Knowledge Distillation

Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning

no code implementations CVPR 2017 Qing Sun, Stefan Lee, Dhruv Batra

We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS) to reason about both forward and backward time dependencies.

Image Captioning

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

24 code implementations7 Oct 2016 Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, Dhruv Batra

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

Image Captioning Machine Translation +4

SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals

no code implementations NeurIPS 2015 Qing Sun, Dhruv Batra

This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image.

Object Proposal Generation

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