research
∙
07/13/2023
Generating Benchmarks for Factuality Evaluation of Language Models
Before deploying a language model (LM) within a given domain, it is impo...
share
research
∙
07/04/2023
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework
Connectionist Temporal Classification (CTC) is a widely used criterion f...
research
∙
04/19/2023
Fundamental Limitations of Alignment in Large Language Models
An important aspect in developing language models that interact with hum...
research
∙
03/14/2023
The Learnability of In-Context Learning
In-context learning is a surprising and important phenomenon that emerge...
research
∙
01/31/2023
In-Context Retrieval-Augmented Language Models
Retrieval-Augmented Language Modeling (RALM) methods, that condition a l...
research
∙
12/21/2022
Parallel Context Windows Improve In-Context Learning of Large Language Models
For applications that require processing large amounts of text at infere...
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
∙
04/06/2022
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks
The field of Natural Language Processing (NLP) has experienced a dramati...
research
∙
10/09/2021
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Pretraining Neural Language Models (NLMs) over a large corpus involves c...
research
∙
05/09/2021
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
After their successful debut in natural language processing, Transformer...
research
∙
03/18/2021
Neural tensor contractions and the expressive power of deep neural quantum states
We establish a direct connection between general tensor networks and dee...
research
∙
06/22/2020
Limits to Depth Efficiencies of Self-Attention
Self-attention architectures, which are rapidly pushing the frontier in ...
research
∙
08/15/2019
SenseBERT: Driving Some Sense into BERT
Self-supervision techniques have allowed neural language models to advan...
research
∙
02/11/2019
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Artificial Neural Networks were recently shown to be an efficient repres...
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
Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks
The harnessing of modern computational abilities for many-body wave-func...
research
∙
10/25/2017
Benefits of Depth for Long-Term Memory of Recurrent Networks
The key attribute that drives the unprecedented success of modern Recurr...
research
∙
10/12/2017
Sum-Product-Quotient Networks
We present a novel tractable generative model that extends Sum-Product 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
∙
05/05/2017
Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions
The driving force behind convolutional networks - the most successful de...
research
∙
04/05/2017
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Deep convolutional networks have witnessed unprecedented success in vari...
research
∙
03/20/2017
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
The driving force behind deep networks is their ability to compactly rep...
research
∙
03/06/2017
On the Expressive Power of Overlapping Architectures of Deep Learning
Expressive efficiency refers to the relation between two architectures A...
research
∙
10/13/2016
Tractable Generative Convolutional Arithmetic Circuits
Casting neural networks in generative frameworks is a highly sought-afte...
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
∙
05/22/2016
Inductive Bias of Deep Convolutional Networks through Pooling Geometry
Our formal understanding of the inductive bias that drives the success o...
research
∙
03/01/2016
Convolutional Rectifier Networks as Generalized Tensor Decompositions
Convolutional rectifier networks, i.e. convolutional neural networks wit...
research
∙
09/16/2015
On the Expressive Power of Deep Learning: A Tensor Analysis
It has long been conjectured that hypotheses spaces suitable for data th...
research
∙
06/09/2015
Deep SimNets
We present a deep layered architecture that generalizes convolutional ne...
research
∙
10/16/2012
Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs
In this paper we present a new approach for tightening upper bounds on t...
research
∙
09/05/2011