Search Results for author: David Cox

Found 31 papers, 19 papers with code

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

1 code implementation4 May 2023 Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable.

Language Modelling

Rapid Development of Compositional AI

no code implementations12 Feb 2023 Lee Martie, Jessie Rosenberg, Veronique Demers, Gaoyuan Zhang, Onkar Bhardwaj, John Henning, Aditya Prasad, Matt Stallone, Ja Young Lee, Lucy Yip, Damilola Adesina, Elahe Paikari, Oscar Resendiz, Sarah Shaw, David Cox

Compositional AI systems, which combine multiple artificial intelligence components together with other application components to solve a larger problem, have no known pattern of development and are often approached in a bespoke and ad hoc style.

ConStruct-VL: Data-Free Continual Structured VL Concepts Learning

1 code implementation CVPR 2023 James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, Leonid Karlinsky

This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting.

VALHALLA: Visual Hallucination for Machine Translation

1 code implementation CVPR 2022 Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu, Chen, Rogerio Feris, David Cox, Nuno Vasconcelos

In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation.

Multimodal Machine Translation Translation

ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers

1 code implementation20 Apr 2022 Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David Cox, Mark Hasegawa-Johnson, Shiyu Chang

Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to downstream tasks.

Disentanglement Self-Supervised Learning

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

1 code implementation NeurIPS 2021 Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan Lin

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i. e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions.

Adversarial Robustness

Global Rhythm Style Transfer Without Text Transcriptions

1 code implementation16 Jun 2021 Kaizhi Qian, Yang Zhang, Shiyu Chang, JinJun Xiong, Chuang Gan, David Cox, Mark Hasegawa-Johnson

In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.

Representation Learning Style Transfer

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

1 code implementation11 Jun 2021 Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan Lin

The key challenges include (1) the dilemma of whether to explode the memory consumption due to the huge joint space or achieve sub-optimal designs, (2) the discrete nature of the accelerator design space that is coupled yet different from that of the networks and bitwidths, and (3) the chicken and egg problem associated with network-accelerator co-search, i. e., co-search requires operation-wise hardware cost, which is lacking during search as the optimal accelerator depending on the whole network is still unknown during search.

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

1 code implementation NeurIPS 2020 Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox, James J. DiCarlo

Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system.

Object Recognition

Lifelong Object Detection

no code implementations2 Sep 2020 Wang Zhou, Shiyu Chang, Norma Sosa, Hendrik Hamann, David Cox

Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.

Knowledge Distillation object-detection +2

Self-supervised Moving Vehicle Tracking with Stereo Sound

no code implementations ICCV 2019 Chuang Gan, Hang Zhao, Peihao Chen, David Cox, Antonio Torralba

At test time, the stereo-sound student network can work independently to perform object localization us-ing just stereo audio and camera meta-data, without any visual input.

Object Localization Visual Localization

ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

1 code implementation NeurIPS 2019 Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox

In this paper, we propose a zeroth-order AdaMM (ZO-AdaMM) algorithm, that generalizes AdaMM to the gradient-free regime.

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

1 code implementation MAPL 2019 Philippe Tillet, H. T. Kung, David Cox

The validation and deployment of novel research ideas in the field of Deep Learning is often limited by the availability of efficient compute kernels for certain basic primitives.

Conditional Infilling GANs for Data Augmentation in Mammogram Classification

1 code implementation21 Jul 2018 Eric Wu, Kevin Wu, David Cox, William Lotter

Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.

Breast Cancer Detection Classification +2

A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception

no code implementations28 May 2018 William Lotter, Gabriel Kreiman, David Cox

Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial.

Open-Ended Question Answering Predict Future Video Frames

A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification

no code implementations21 Jul 2017 William Lotter, Greg Sorensen, David Cox

Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone.

General Classification

Recurrent computations for visual pattern completion

1 code implementation7 Jun 2017 Hanlin Tang, Martin Schrimpf, Bill Lotter, Charlotte Moerman, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David Cox, Gabriel Kreiman

First, subjects robustly recognized objects even when rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking.

Image Classification

Using Human Brain Activity to Guide Machine Learning

no code implementations16 Mar 2017 Ruth Fong, Walter Scheirer, David Cox

The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

BIG-bench Machine Learning Object Recognition

Tensor Switching Networks

1 code implementation NeurIPS 2016 Chuan-Yung Tsai, Andrew Saxe, David Cox

We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units.

Representation Learning

Delta Epsilon Alpha Star: A PAC-Admissible Search Algorithm

no code implementations8 Aug 2016 David Cox

Delta Epsilon Alpha Star is a minimal coverage, real-time robotic search algorithm that yields a moderately aggressive search path with minimal backtracking.

Syntactically Informed Text Compression with Recurrent Neural Networks

1 code implementation8 Aug 2016 David Cox

We present a self-contained system for constructing natural language models for use in text compression.

Text Compression

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

17 code implementations25 May 2016 William Lotter, Gabriel Kreiman, David Cox

Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world.

Object Recognition Video Prediction

Unsupervised Learning of Visual Structure using Predictive Generative Networks

2 code implementations19 Nov 2015 William Lotter, Gabriel Kreiman, David Cox

The ability to predict future states of the environment is a central pillar of intelligence.

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