Search Results for author: Palash Goyal

Found 19 papers, 9 papers with code

Leveraging Local Temporal Information for Multimodal Scene Classification

no code implementations26 Oct 2021 Saurabh Sahu, Palash Goyal

In this paper, we propose a novel self-attention block that leverages both local and global temporal relationships between the video frames to obtain better contextualized representations for the individual frames.

Classification Frame +2

Can't Fool Me: Adversarially Robust Transformer for Video Understanding

no code implementations26 Oct 2021 Divya Choudhary, Palash Goyal, Saurabh Sahu

To address this, several techniques have been proposed to increase robustness of a model for image classification tasks.

Image Classification Video Understanding

Hierarchical Class-Based Curriculum Loss

no code implementations5 Jun 2020 Palash Goyal, Shalini Ghosh

We theoretically show that the proposed loss function is a tighter bound of 0-1 loss compared to any other loss satisfying the hierarchical constraints.

Cross-modal Learning for Multi-modal Video Categorization

no code implementations7 Mar 2020 Palash Goyal, Saurabh Sahu, Shalini Ghosh, Chul Lee

Multi-modal machine learning (ML) models can process data in multiple modalities (e. g., video, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding, activity recognition).

Activity Recognition Object Detection +1

Exploiting Temporal Coherence for Multi-modal Video Categorization

no code implementations7 Feb 2020 Palash Goyal, Saurabh Sahu, Shalini Ghosh, Chul Lee

Multimodal ML models can process data in multiple modalities (e. g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding).

Object Detection Scene Understanding

ArduCode: Predictive Framework for Automation Engineering

no code implementations6 Sep 2019 Arquimedes Canedo, Palash Goyal, Di Huang, Amit Pandey, Gustavo Quiros

We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.

Decision Making

Graph Representation Ensemble Learning

1 code implementation6 Sep 2019 Palash Goyal, Di Huang, Sujit Rokka Chhetri, Arquimedes Canedo, Jaya Shree, Evan Patterson

In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.

Ensemble Learning Graph Embedding +1

Benchmarks for Graph Embedding Evaluation

1 code implementation19 Aug 2019 Palash Goyal, Di Huang, Ankita Goswami, Sujit Rokka Chhetri, Arquimedes Canedo, Emilio Ferrara

We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.

Graph Embedding Link Prediction

Tracking Temporal Evolution of Graphs using Non-Timestamped Data

1 code implementation4 Jul 2019 Sujit Rokka Chhetri, Palash Goyal, Arquimedes Canedo

Datasets to study the temporal evolution of graphs are scarce.

Social and Information Networks

DynGEM: Deep Embedding Method for Dynamic Graphs

1 code implementation29 May 2018 Palash Goyal, Nitin Kamra, Xinran He, Yan Liu

The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph.

Social and Information Networks

Capturing Edge Attributes via Network Embedding

1 code implementation8 May 2018 Palash Goyal, Homa Hosseinmardi, Emilio Ferrara, Aram Galstyan

Here we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations.

Social and Information Networks

Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach

no code implementations26 Apr 2018 Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan

We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.

Dialogue Generation Model Selection

Graph Embedding Techniques, Applications, and Performance: A Survey

3 code implementations8 May 2017 Palash Goyal, Emilio Ferrara

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.

Graph Embedding

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