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Found 28 papers, 17 papers with code
Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis
We demonstrate a spiking neural network for navigation motivated by the chemotaxis network of Caenorhabditis elegans.
A neural circuit for navigation inspired by C. elegans Chemotaxis
In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations.
Deep Tensor Convolution on Multicores
Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features.
Toward Streaming Synapse Detection with Compositional ConvNets
Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images.
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed.
A Classification-Based Study of Covariate Shift in GAN Distributions
A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on.
A Classification-Based Perspective on GAN Distributions
A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on.
Adversarially Robust Generalization Requires More Data
We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.
How Does Batch Normalization Help Optimization?
Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs).
Robustness May Be at Odds with Accuracy
We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization.
A Closer Look at Deep Policy Gradients
We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.
Adversarial Examples Are Not Bugs, They Are Features
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear.
Adversarial Robustness as a Prior for Learned Representations
In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks.
Image Synthesis with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis.
Ranked #60 on
Image Generation
on CIFAR-10
(Inception score metric)
Implementation Matters in Deep RL: A Case Study on PPO and TRPO
We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms, Proximal Policy Optimization and Trust Region Policy Optimization.
Identifying Statistical Bias in Dataset Replication
We study ImageNet-v2, a replication of the ImageNet dataset on which models exhibit a significant (11-14%) drop in accuracy, even after controlling for a standard human-in-the-loop measure of data quality.
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline.
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO).
BREEDS: Benchmarks for Subpopulation Shift
We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training.
Leveraging Sparse Linear Layers for Debuggable Deep Networks
We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks.
3DB: A Framework for Debugging Computer Vision Models
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
Editing a classifier by rewriting its prediction rules
We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules.
Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning
The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Data Selection for Language Models via Importance Resampling
To measure whether hashed n-gram features preserve the aspects of the data that are relevant to the target, we define KL reduction, a data metric that measures the proximity between the selected pretraining data and the target on some feature space.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Whose Opinions Do Language Models Reflect?
Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large.
Statistical Bias in Dataset Replication
Dataset replication is a useful tool for assessing whether models have overfit to a specific validation set or the exact circumstances under which it was generated.
