Search Results for author: Soham Dan

Found 16 papers, 1 papers with code

Goodness-of-Fit Tests for Inhomogeneous Random Graphs

no code implementations ICML 2020 Soham Dan, Bhaswar B. Bhattacharya

Hypothesis testing of random networks is an emerging area of modern research, especially in the high-dimensional regime, where the number of samples is smaller or comparable to the size of the graph.

Two-sample testing

On the Effects of Transformer Size on In- and Out-of-Domain Calibration

no code implementations Findings (EMNLP) 2021 Soham Dan, Dan Roth

To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks.

Natural Language Processing

Compositional Data and Task Augmentation for Instruction Following

no code implementations Findings (EMNLP) 2021 Soham Dan, Xinran Han, Dan Roth

Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them.

Few-Shot Novel Concept Learning for Semantic Parsing

no code implementations Findings (EMNLP) 2021 Soham Dan, Osbert Bastani, Dan Roth

This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept.

Program Synthesis Semantic Parsing

Understanding Robust Generalization in Learning Regular Languages

no code implementations20 Feb 2022 Soham Dan, Osbert Bastani, Dan Roth

Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.

Investigating the locality of neural network training dynamics

no code implementations1 Nov 2021 Soham Dan, Phanideep Gampa, Anirbit Mukherjee

In the recent past a certain property of neural training trajectories in weight-space had been isolated, that of "local elasticity" ($\srel$) - which attempts to quantify the propagation of influence of a sampled data point on the prediction at another data point.

Generalization in Instruction Following Systems

no code implementations NAACL 2021 Soham Dan, Michael Zhou, Dan Roth

Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence.

Data Augmentation

A Locally Linear Procedure for Word Translation

no code implementations COLING 2020 Soham Dan, Hagai Taitelbaum, Jacob Goldberger

We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices.

Translation Word Embeddings +2

Understanding Spatial Relations through Multiple Modalities

no code implementations LREC 2020 Soham Dan, Hangfeng He, Dan Roth

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.

Common Sense Reasoning

From Spatial Relations to Spatial Configurations

no code implementations LREC 2020 Soham Dan, Parisa Kordjamshidi, Julia Bonn, Archna Bhatia, Jon Cai, Martha Palmer, Dan Roth

To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

Natural Language Understanding

Learning from Noisy Similar and Dissimilar Data

no code implementations3 Feb 2020 Soham Dan, Han Bao, Masashi Sugiyama

We perform a detailed investigation of this problem under two realistic noise models and propose two algorithms to learn from noisy S-D data.

Variance Reduced Stochastic Proximal Algorithm for AUC Maximization

no code implementations8 Nov 2019 Soham Dan, Dushyant Sahoo

To combat this issue, several variance reduced methods have been proposed with faster convergence guarantees than vanilla stochastic gradient descent.

AppTechMiner: Mining Applications and Techniques from Scientific Articles

no code implementations10 Sep 2017 Mayank Singh, Soham Dan, Sanyam Agarwal, Pawan Goyal, Animesh Mukherjee

We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article.

Information Retrieval

Which techniques does your application use?: An information extraction framework for scientific articles

no code implementations23 Aug 2016 Soham Dan, Sanyam Agarwal, Mayank Singh, Pawan Goyal, Animesh Mukherjee

Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas.

Language Modelling

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