Search Results for author:
Found 18 papers, 12 papers with code
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
Models pretrained on ImageNet+ and fine-tuned on CIFAR-100+, Flowers-102+, and Food-101+, reach up to 3. 4% improved accuracy.
FastFill: Efficient Compatible Model Update
The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features.
RangeAugment: Efficient Online Augmentation with Range Learning
To answer the open question on the importance of magnitude ranges for each augmentation operation, we introduce RangeAugment that allows us to efficiently learn the range of magnitudes for individual as well as composite augmentation operations.
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
Recent advances in learning aligned multimodal representations have been primarily driven by training large neural networks on massive, noisy paired-modality datasets.
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks
We introduce MixTailor, a scheme based on randomization of the aggregation strategies that makes it impossible for the attacker to be fully informed.
Training Efficiency and Robustness in Deep Learning
We show that a redundancy-aware modification to the sampling of training data improves the training speed and develops an efficient method for detecting the diversity of training signal, namely, gradient clustering.
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training.
Bridging the Gap Between Adversarial Robustness and Optimization Bias
We demonstrate that the choice of optimizer, neural network architecture, and regularizer significantly affect the adversarial robustness of linear neural networks, providing guarantees without the need for adversarial training.
Adaptive Gradient Quantization for Data-Parallel SGD
Many communication-efficient variants of SGD use gradient quantization schemes.
A Study of Gradient Variance in Deep Learning
We introduce a method, Gradient Clustering, to minimize the variance of average mini-batch gradient with stratified sampling.
SOAR: Second-Order Adversarial Regularization
Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples.
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed
In this paper, we present a non-asymptotic analysis of SVRG under a noisy least squares regression problem.
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel.
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel.
Adversarial Spheres
We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.
Ranked #20 on
Cross-Modal Retrieval
on Flickr30k
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Adversarial Manipulation of Deep Representations
We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image.
