b'Roy Schwartz'

research

∙ 08/15/2023

A Tight Competitive Ratio for Online Submodular Welfare Maximization

In this paper we consider the online Submodular Welfare (SW) problem. In...

0 Amit Ganz, et al. ∙

research

∙ 08/07/2023

An Improved Approximation Algorithm for the Max-3-Section Problem

We consider the Max-3-Section problem, where we are given an undirected ...

0 Dor Katzelnick, et al. ∙

research

∙ 07/06/2023

Read, Look or Listen? What's Needed for Solving a Multimodal Dataset

The prevalence of large-scale multimodal datasets presents unique challe...

0 Netta Madvil, et al. ∙

research

∙ 06/29/2023

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research

Many recent improvements in NLP stem from the development and use of lar...

0 Ji-Ung Lee, et al. ∙

research

∙ 06/09/2023

Morphosyntactic probing of multilingual BERT models

We introduce an extensive dataset for multilingual probing of morphologi...

0 Judit Acs, et al. ∙

research

∙ 06/04/2023

Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings

Adaptive inference is a simple method for reducing inference costs. The ...

0 Daniel Rotem, et al. ∙

research

∙ 05/30/2023

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases

NLP models often rely on superficial cues known as dataset biases to ach...

0 Yuval Reif, et al. ∙

research

∙ 05/22/2023

Textually Pretrained Speech Language Models

Speech language models (SpeechLMs) process and generate acoustic data on...

0 Michael Hassid, et al. ∙

research

∙ 03/13/2023

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Weird, unusual, and uncanny images pique the curiosity of observers beca...

0 Nitzan Bitton Guetta, et al. ∙

research

∙ 12/08/2022

VASR: Visual Analogies of Situation Recognition

A core process in human cognition is analogical mapping: the ability to ...

0 Yonatan Bitton, et al. ∙

research

∙ 11/07/2022

How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers

The attention mechanism is considered the backbone of the widely-used Tr...

0 Michael Hassid, et al. ∙

research

∙ 08/31/2022

Efficient Methods for Natural Language Processing: A Survey

Getting the most out of limited resources allows advances in natural lan...

9 Marcos Treviso, et al. ∙

research

∙ 07/25/2022

WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models

While vision-and-language models perform well on tasks such as visual qu...

1 Yonatan Bitton, et al. ∙

research

∙ 06/20/2022

Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias

The size of pretrained models is increasing, and so is their performance...

0 Yarden Tal, et al. ∙

research

∙ 06/10/2022

Measuring the Carbon Intensity of AI in Cloud Instances

By providing unprecedented access to computational resources, cloud comp...

0 Jesse Dodge, et al. ∙

research

∙ 04/27/2022

On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations

Recent work has shown that deep learning models in NLP are highly sensit...

0 Roy Schwartz, et al. ∙

research

∙ 04/13/2022

TangoBERT: Reducing Inference Cost by using Cascaded Architecture

The remarkable success of large transformer-based models such as BERT, R...

6 Jonathan Mamou, et al. ∙

research

∙ 04/05/2022

A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography

Purpose - To develop and validate a deep learning (DL) framework for the...

0 Roy Schwartz, et al. ∙

research

∙ 03/15/2022

Data Contamination: From Memorization to Exploitation

Pretrained language models are typically trained on massive web-based da...

0 Inbal Magar, et al. ∙

research

∙ 10/06/2021

ABC: Attention with Bounded-memory Control

Transformer architectures have achieved state-of-the-art results on a va...

0 Hao Peng, et al. ∙

research

∙ 10/01/2021

Expected Validation Performance and Estimation of a Random Variable's Maximum

Research in NLP is often supported by experimental results, and improved...

0 Jesse Dodge, et al. ∙

research

∙ 09/05/2021

Data Efficient Masked Language Modeling for Vision and Language

Masked language modeling (MLM) is one of the key sub-tasks in vision-lan...

0 Yonatan Bitton, et al. ∙

research

∙ 06/10/2021

Graph Balancing with Orientation Costs

Motivated by the classic Generalized Assignment Problem, we consider the...

0 Roy Schwartz, et al. ∙

research

∙ 05/03/2021

Fault Tolerant Max-Cut

In this work, we initiate the study of fault tolerant Max Cut, where giv...

0 Keren Censor-Hillel, et al. ∙

research

∙ 04/23/2021

The Metric Relaxation for 0-Extension Admits an Ω(log^2/3k) Gap

We consider the 0-Extension problem, where we are given an undirected gr...

0 Roy Schwartz, et al. ∙

research

∙ 04/22/2021

Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

Language models trained on billions of tokens have recently led to unpre...

0 William Merrill, et al. ∙

research

∙ 03/17/2021

Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA

Recent works have shown that supervised models often exploit data artifa...

0 Yonatan Bitton, et al. ∙

research

∙ 03/03/2021

Random Feature Attention

Transformers are state-of-the-art models for a variety of sequence model...

0 Hao Peng, et al. ∙

research

∙ 02/25/2021

A Refined Analysis of Submodular Greedy

Many algorithms for maximizing a monotone submodular function subject to...

0 Ariel Kulik, et al. ∙

research

∙ 10/19/2020

Parameter Norm Growth During Training of Transformers

The capacity of neural networks like the widely adopted transformer is k...

0 William Merrill, et al. ∙

research

∙ 10/08/2020

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

The urgency of mitigating COVID-19 has spawned a large and diverse body ...

0 Aida Amini, et al. ∙

research

∙ 05/13/2020

A Mixture of h-1 Heads is Better than h Heads

Multi-head attentive neural architectures have achieved state-of-the-art...

0 Hao Peng, et al. ∙

research

∙ 04/18/2020

A Formal Hierarchy of RNN Architectures

We develop a formal hierarchy of the expressive capacity of RNN architec...

0 William Merrill, et al. ∙

research

∙ 04/16/2020

The Right Tool for the Job: Matching Model and Instance Complexities

As NLP models become larger, executing a trained model requires signific...

0 Roy Schwartz, et al. ∙

research

∙ 02/15/2020

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Fine-tuning pretrained contextual word embedding models to supervised do...

0 Jesse Dodge, et al. ∙

research

∙ 09/09/2019

Knowledge Enhanced Contextual Word Representations

Contextual word representations, typically trained on unstructured, unla...

0 Matthew E. Peters, et al. ∙

research

∙ 09/06/2019

RNN Architecture Learning with Sparse Regularization

Neural models for NLP typically use large numbers of parameters to reach...

13 Jesse Dodge, et al. ∙

research

∙ 09/06/2019

Show Your Work: Improved Reporting of Experimental Results

Research in natural language processing proceeds, in part, by demonstrat...

26 Jesse Dodge, et al. ∙

research

∙ 09/04/2019

PaLM: A Hybrid Parser and Language Model

We present PaLM, a hybrid parser and neural language model. Building on ...

0 Hao Peng, et al. ∙

research

∙ 07/22/2019

Green AI

The computations required for deep learning research have been doubling ...

4 Roy Schwartz, et al. ∙

research

∙ 06/28/2019

Min-Max Correlation Clustering via MultiCut

Correlation clustering is a fundamental combinatorial optimization probl...

0 Saba Ahmadi, et al. ∙

research

∙ 05/07/2019

Online and Offline Greedy Algorithms for Routing with Switching Costs

Motivated by the use of high speed circuit switches in large scale data ...

0 Roy Schwartz, et al. ∙

research

∙ 04/04/2019

Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets

Several datasets have recently been constructed to expose brittleness in...

0 Nelson F. Liu, et al. ∙

research

∙ 12/19/2018

Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems

Semidefinite programming is a powerful tool in the design and analysis o...

0 Sepehr Abbasi-Zadeh, et al. ∙

research

∙ 08/28/2018

Rational Recurrences

Despite the tremendous empirical success of neural models in natural lan...

0 Hao Peng, et al. ∙

research

∙ 08/16/2018

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Given a partial description like "she opened the hood of the car," human...

0 Rowan Zellers, et al. ∙

research

∙ 05/29/2018

LSTMs Exploit Linguistic Attributes of Data

While recurrent neural networks have found success in a variety of natur...

0 Nelson F. Liu, et al. ∙

research

∙ 05/15/2018

SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines

Recurrent and convolutional neural networks comprise two distinct famili...

0 Roy Schwartz, et al. ∙

research

∙ 04/29/2018

A Tight Approximation for Submodular Maximization with Mixed Packing and Covering Constraints

Motivated by applications in machine learning, such as subset selection ...

0 Eyal Mizrachi, et al. ∙

research

∙ 04/25/2018

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

Peer reviewing is a central component in the scientific publishing proce...

0 Dongyeop Kang, et al. ∙

Roy Schwartz

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