Search Results for author: Felix Wu

Found 26 papers, 20 papers with code

A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks

2 code implementations18 May 2023 Yifan Peng, Kwangyoun Kim, Felix Wu, Brian Yan, Siddhant Arora, William Chen, Jiyang Tang, Suwon Shon, Prashant Sridhar, Shinji Watanabe

Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding

no code implementations27 Feb 2023 Yifan Peng, Kwangyoun Kim, Felix Wu, Prashant Sridhar, Shinji Watanabe

Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow.

Model Compression Representation Learning +2

SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks

no code implementations20 Dec 2022 Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, Ankita Pasad, Felix Wu, Roshan Sharma, Wei-Lun Wu, Hung-Yi Lee, Karen Livescu, Shinji Watanabe

In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape.

Dialog Act Classification Question Answering +4

Context-aware Fine-tuning of Self-supervised Speech Models

no code implementations16 Dec 2022 Suwon Shon, Felix Wu, Kwangyoun Kim, Prashant Sridhar, Karen Livescu, Shinji Watanabe

During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

E-Branchformer: Branchformer with Enhanced merging for speech recognition

1 code implementation30 Sep 2022 Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan, Prashant Sridhar, Kyu J. Han, Shinji Watanabe

Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

On the Use of External Data for Spoken Named Entity Recognition

1 code implementation NAACL 2022 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 +6

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 Automatic Speech Recognition (ASR) +1

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.

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 TriviaQA

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 Retrieval

Simplifying Graph Convolutional Networks

7 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.

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

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