research
∙
02/13/2023
SubTuning: Efficient Finetuning for Multi-Task Learning
Finetuning a pretrained model has become a standard approach for trainin...
share
research
∙
05/01/2022
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
Huge language models (LMs) have ushered in a new era for AI, serving as ...
research
∙
03/28/2022
Knowledge Distillation: Bad Models Can Be Good Role Models
Large neural networks trained in the overparameterized regime are able t...
research
∙
01/31/2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
Several recent works have shown separation results between deep neural n...
research
∙
10/03/2020
Computational Separation Between Convolutional and Fully-Connected Networks
Convolutional neural networks (CNN) exhibit unmatched performance in a m...
research
∙
08/18/2020
When Hardness of Approximation Meets Hardness of Learning
A supervised learning algorithm has access to a distribution of labeled ...
research
∙
02/03/2020
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
The lottery ticket hypothesis (Frankle and Carbin, 2018), states that a ...
research
∙
10/25/2019
Learning Boolean Circuits with Neural Networks
Training neural-networks is computationally hard. However, in practice t...
research
∙
09/26/2019
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
A leading hypothesis for the surprising generalization of neural network...
research
∙
08/15/2019
SenseBERT: Driving Some Sense into BERT
Self-supervision techniques have allowed neural language models to advan...
research
∙
07/15/2019
Discriminative Active Learning
We propose a new batch mode active learning algorithm designed for neura...
research
∙
06/12/2019
Decoupling Gating from Linearity
ReLU neural-networks have been in the focus of many recent theoretical w...
research
∙
03/08/2019
Is Deeper Better only when Shallow is Good?
Understanding the power of depth in feed-forward neural networks is an o...
research
∙
12/09/2018
Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput
We propose an economical, viable, approach to eliminate almost all car a...
research
∙
03/26/2018
A Provably Correct Algorithm for Deep Learning that Actually Works
We describe a layer-by-layer algorithm for training deep convolutional n...
research
∙
08/21/2017
On a Formal Model of Safe and Scalable Self-driving Cars
In recent years, car makers and tech companies have been racing towards ...
research
∙
03/23/2017
Failures of Gradient-Based Deep Learning
In recent years, Deep Learning has become the go-to solution for a broad...
research
∙
10/11/2016
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Autonomous driving is a multi-agent setting where the host vehicle must ...
research
∙
05/24/2016
Learning a Metric Embedding for Face Recognition using the Multibatch Method
This work is motivated by the engineering task of achieving a near state...
research
∙
11/13/2014
SelfieBoost: A Boosting Algorithm for Deep Learning
We describe and analyze a new boosting algorithm for deep learning calle...
research
∙
10/05/2014
On the Computational Efficiency of Training Neural Networks
It is well-known that neural networks are computationally hard to train....
research
∙
02/19/2014
Subspace Learning with Partial Information
The goal of subspace learning is to find a k-dimensional subspace of R^d...
research
∙
09/10/2013
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
We introduce a proximal version of the stochastic dual coordinate ascent...
research
∙
05/12/2013
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Stochastic dual coordinate ascent (SDCA) is an effective technique for s...
research
∙
04/26/2013
An Algorithm for Training Polynomial Networks
We consider deep neural networks, in which the output of each node is a ...
research
∙
12/13/2012
Learning Sparse Low-Threshold Linear Classifiers
We consider the problem of learning a non-negative linear classifier wit...
research
∙
11/12/2012
Proximal Stochastic Dual Coordinate Ascent
We introduce a proximal version of dual coordinate ascent method. We dem...
research
∙
09/10/2012
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Gradient Descent (SGD) has become popular for solving large s...
research
∙
06/18/2012
Learning the Experts for Online Sequence Prediction
Online sequence prediction is the problem of predicting the next element...
research
∙
12/07/2011
Active Learning of Halfspaces under a Margin Assumption
We derive and analyze a new, efficient, pool-based active learning algor...
research
∙
09/05/2011
ShareBoost: Efficient Multiclass Learning with Feature Sharing
Multiclass prediction is the problem of classifying an object into a rel...
research
∙
06/08/2011
Large-Scale Convex Minimization with a Low-Rank Constraint
We address the problem of minimizing a convex function over the space of...
research
∙
06/06/2011
Using More Data to Speed-up Training Time
In many recent applications, data is plentiful. By now, we have a rather...
research
∙
10/04/2009