Search Results for author: Le Hou

Found 23 papers, 9 papers with code

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

1 code implementation31 Jan 2023 Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts

We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation

1 code implementation21 Oct 2022 Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji

However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models).

Data Augmentation Knowledge Distillation

Large Language Models Can Self-Improve

no code implementations20 Oct 2022 Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han

We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74. 4%->82. 1% on GSM8K, 78. 2%->83. 0% on DROP, 90. 0%->94. 4% on OpenBookQA, and 63. 4%->67. 9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label.

Arithmetic Reasoning Common Sense Reasoning +2

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

no code implementations21 May 2022 Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, Ed Chi

Although chain-of-thought prompting has shown impressive results on many natural language reasoning tasks, it often performs poorly on tasks which need to solve problems harder than the demonstration examples.

Arithmetic Reasoning

Speeding up Deep Model Training by Sharing Weights and Then Unsharing

no code implementations8 Oct 2021 Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

Our approach exploits the special structure of BERT that contains a stack of repeated modules (i. e., transformer encoders).

Speeding up Deep Learning Training by Sharing Weights and Then Unsharing

no code implementations1 Jan 2021 Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks.

Talking-Heads Attention

4 code implementations5 Mar 2020 Noam Shazeer, Zhenzhong Lan, Youlong Cheng, Nan Ding, Le Hou

We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation. While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.

Language Modelling Masked Language Modeling +2

Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types

1 code implementation18 Feb 2020 Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz

To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.

Exascale Deep Learning to Accelerate Cancer Research

no code implementations26 Sep 2019 Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou, Shahira Abousamra, Dimitris Samaras, Joel Saltz

Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference.

Neural Architecture Search

High Resolution Medical Image Analysis with Spatial Partitioning

1 code implementation6 Sep 2019 Le Hou, Youlong Cheng, Noam Shazeer, Niki Parmar, Yeqing Li, Panagiotis Korfiatis, Travis M. Drucker, Daniel J. Blezek, Xiaodan Song

It is infeasible to train CNN models directly on such high resolution images, because neural activations of a single image do not fit in the memory of a single GPU/TPU, and naive data and model parallelism approaches do not work.

Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types

no code implementations9 Jul 2019 Shahira Abousamra, Le Hou, Rajarsi Gupta, Chao Chen, Dimitris Samaras, Tahsin Kurc, Rebecca Batiste, Tianhao Zhao, Shroyer Kenneth, Joel Saltz

This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment.

General Classification

Robust Histopathology Image Analysis: To Label or to Synthesize?

no code implementations CVPR 2019 Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurc, Rajarsi R. Gupta, Joel H. Saltz

In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures.

Image Segmentation Semantic Segmentation +1

Label super-resolution networks

no code implementations ICLR 2019 Kolya Malkin, Caleb Robinson, Le Hou, Nebojsa Jojic

We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels.

Semantic Segmentation Super-Resolution

Label Super Resolution with Inter-Instance Loss

no code implementations9 Apr 2019 Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin Kurc, Rajarsi Gupta, Kolya Malkin, Shroyer Kenneth, Joel Saltz

On the other hand, collecting low resolution labels (labels for a block of pixels) for these high resolution images is much more cost efficient.

Semantic Segmentation Super-Resolution

Unsupervised Histopathology Image Synthesis

no code implementations13 Dec 2017 Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurc, Rajarsi R. Gupta, Joel H. Saltz

We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.

Image Generation

Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images

no code implementations3 Apr 2017 Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Tianhao Zhao, Joel H. Saltz

In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images.

Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images

no code implementations20 Dec 2016 Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz

Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease.

General Classification

Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks

3 code implementations17 Nov 2016 Le Hou, Chen-Ping Yu, Dimitris Samaras

In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification.

General Classification

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

1 code implementation CVPR 2016 Le Hou, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, James E. Davis, Joel H. Saltz

However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible.

Classification General Classification +1

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