no code implementations • 19 Dec 2023 • Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
Our paper is the first to link LLM misgendering to tokenization and deficient neopronoun grammar, indicating that LLMs unable to correctly treat neopronouns as pronouns are more prone to misgender.
no code implementations • 19 Dec 2023 • Palash Goyal, Qian Hu, Rahul Gupta
Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship.
no code implementations • 16 Nov 2023 • Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance.
no code implementations • 8 Nov 2023 • Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.
no code implementations • 8 Aug 2023 • Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.
no code implementations • 17 May 2023 • Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
no code implementations • 17 Nov 2022 • Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
Natural language often contains ambiguities that can lead to misinterpretation and miscommunication.
no code implementations • 26 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.
no code implementations • 26 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.
no code implementations • 18 Mar 2021 • Saurabh Sahu, Palash Goyal
GAT uses a multi-level attention gate to model the relevance of a frame based on local and global contexts.
no code implementations • 5 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.
no code implementations • 7 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).
no code implementations • 7 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).
1 code implementation • 6 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.
no code implementations • 6 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.
1 code implementation • 19 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.
1 code implementation • 4 Jul 2019 • Sujit Rokka Chhetri, Palash Goyal, Arquimedes Canedo
Datasets to study the temporal evolution of graphs are scarce.
Social and Information Networks
1 code implementation • 4 Jun 2019 • Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque
Python library for knowledge graph embedding and representation learning.
1 code implementation • 26 Nov 2018 • Palash Goyal, Sujit Rokka Chhetri, Ninareh Mehrabi, Emilio Ferrara, Arquimedes Canedo
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
1 code implementation • 7 Sep 2018 • Palash Goyal, Sujit Rokka Chhetri, Arquimedes Canedo
Capturing such evolution is key to predicting the properties of unseen networks.
Ranked #5 on Dynamic Link Prediction on Enron Emails
no code implementations • 24 Aug 2018 • Jiang Wan, Blake S. Pollard, Sujit Rokka Chhetri, Palash Goyal, Mohammad Abdullah Al Faruque, Arquimedes Canedo
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs.
no code implementations • 8 Jun 2018 • Palash Goyal, KSM Tozammel Hossain, Ashok Deb, Nazgol Tavabi, Nathan Bartley, Andr'es Abeliuk, Emilio Ferrara, Kristina Lerman
Cyber attacks are growing in frequency and severity.
1 code implementation • 29 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
1 code implementation • 8 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
no code implementations • 26 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.
3 code implementations • 8 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.