Search Results for author: Felix Wu

Found 20 papers, 16 papers with code

On the Use of External Data for Spoken Named Entity Recognition

no code implementations14 Dec 2021 Ankita Pasad, Felix Wu, Suwon Shon, Karen Livescu, Kyu J. Han

In this work we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond self-supervised pre-training, how can we use external speech and/or text data that are not annotated for the task?

Knowledge Distillation named-entity-recognition +4

Multi-mode Transformer Transducer with Stochastic Future Context

no code implementations17 Jun 2021 Kwangyoun Kim, Felix Wu, Prashant Sridhar, Kyu J. Han, Shinji Watanabe

A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy.

Automatic Speech Recognition

Making Paper Reviewing Robust to Bid Manipulation Attacks

1 code implementation9 Feb 2021 Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger

We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.

Attention-based Quantum Tomography

1 code implementation22 Jun 2020 Peter Cha, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, Eun-Ah Kim

Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.

Natural Language Processing

Revisiting Few-sample BERT Fine-tuning

1 code implementation ICLR 2021 Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, Yoav Artzi

We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.

On Feature Normalization and Data Augmentation

1 code implementation CVPR 2021 Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger

The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.

Data Augmentation Domain Generalization +2

Integrated Triaging for Fast Reading Comprehension

no code implementations28 Sep 2019 Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger

This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.

Machine Reading Comprehension

Positional Normalization

2 code implementations NeurIPS 2019 Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie

A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.

FastFusionNet: New State-of-the-Art for DAWNBench SQuAD

2 code implementations28 Feb 2019 Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger

In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].

Reading Comprehension

Simplifying Graph Convolutional Networks

6 code implementations19 Feb 2019 Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

Ranked #3 on Text Classification on 20NEWS (using extra training data)

Graph Regression Image Classification +5

Attack Graph Convolutional Networks by Adding Fake Nodes

no code implementations ICLR 2019 Xiaoyun Wang, Minhao Cheng, Joe Eaton, Cho-Jui Hsieh, Felix Wu

In this paper, we propose a new type of "fake node attacks" to attack GCNs by adding malicious fake nodes.

On Fairness and Calibration

1 code implementation NeurIPS 2017 Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger

The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models.

Fairness General Classification

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