Search Results for author: Ajay Divakaran

Found 33 papers, 4 papers with code

System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games

no code implementations8 Dec 2022 Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan

In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.

Continual Learning reinforcement-learning +2

Unpacking Large Language Models with Conceptual Consistency

no code implementations29 Sep 2022 Pritish Sahu, Michael Cogswell, Yunye Gong, Ajay Divakaran

The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query.

Language Modelling

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2

no code implementations9 Aug 2022 Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran

We present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning.

Management reinforcement-learning +3

Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction

no code implementations15 Jul 2022 Sujeong Kim, Abhinav Garlapati, Jonah Lubin, Amir Tamrakar, Ajay Divakaran

We present a series of two studies conducted to understand user's affective states during voice-based human-machine interactions.

Detecting out-of-context objects using contextual cues

no code implementations11 Feb 2022 Manoj Acharya, Anirban Roy, Kaushik Koneripalli, Susmit Jha, Christopher Kanan, Ajay Divakaran

GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects.

Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark

no code implementations22 Oct 2021 Pritish Sahu, Karan Sikka, Ajay Divakaran

We also observe a drop in performance across all the models when testing on RecipeQA and proposed Meta-RecipeQA (e. g. 83. 6% versus 67. 1% for HTRN), which shows that the proposed dataset is relatively less biased.

Answer Generation Machine Reading Comprehension +2

Towards Solving Multimodal Comprehension

no code implementations20 Apr 2021 Pritish Sahu, Karan Sikka, Ajay Divakaran

We then evaluate M3C using a textual cloze style question-answering task and highlight an inherent bias in the question answer generation method from [35] that enables a naive baseline to cheat by learning from only answer choices.

Answer Generation Question-Answer-Generation +2

Modular Adaptation for Cross-Domain Few-Shot Learning

1 code implementation1 Apr 2021 Xiao Lin, Meng Ye, Yunye Gong, Giedrius Buracas, Nikoletta Basiou, Ajay Divakaran, Yi Yao

Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.

cross-domain few-shot learning Representation Learning

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer

no code implementations14 Jul 2020 Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.

Continual Learning Transfer Learning

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer

no code implementations ICML Workshop LifelongML 2020 Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.

Continual Learning Starcraft +1

Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks

no code implementations16 Mar 2020 Karan Sikka, Andrew Silberfarb, John Byrnes, Indranil Sur, Ed Chow, Ajay Divakaran, Richard Rohwer

We introduce Deep Adaptive Semantic Logic (DASL), a novel framework for automating the generation of deep neural networks that incorporates user-provided formal knowledge to improve learning from data.

Image Classification Visual Relationship Detection

Progressive Growing of Neural ODEs

no code implementations ICLR Workshop DeepDiffEq 2019 Hammad A. Ayyubi, Yi Yao, Ajay Divakaran

Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data.

Time Series Forecasting

A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing

no code implementations25 Sep 2019 Xiao Lin, Indranil Sur, Samuel A. Nastase, Uri Hasson, Ajay Divakaran, Mohamed R. Amer

Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.


FoodX-251: A Dataset for Fine-grained Food Classification

1 code implementation14 Jul 2019 Parneet Kaur, Karan Sikka, Weijun Wang, Serge Belongie, Ajay Divakaran

Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models.

Classification Fine-Grained Visual Categorization +1

Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks

no code implementations17 May 2019 Karan Sikka, Lucas Van Bramer, Ajay Divakaran

We also show that the user embeddings learned within our joint multimodal embedding model are better at predicting user interests compared to those learned with unimodal content on Instagram data.

Cross-Modal Retrieval Retrieval

Data-Efficient Mutual Information Neural Estimator

no code implementations8 May 2019 Xiao Lin, Indranil Sur, Samuel A. Nastase, Ajay Divakaran, Uri Hasson, Mohamed R. Amer

We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.


Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts

1 code implementation IJCNLP 2019 Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran

Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image.

Intent Detection

Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

no code implementations5 Apr 2019 Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, Giedrius Burachas

Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.

Image Retrieval Question Answering +2

Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation

no code implementations26 Nov 2018 Pallabi Ghosh, Yi Yao, Larry S. Davis, Ajay Divakaran

We show results on CAD120 (which provides pre-computed node features and edge weights for fair performance comparison across algorithms) as well as a more complex real-world activity dataset, Charades.

Action Recognition Action Segmentation +2

Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention

no code implementations4 Jul 2018 Karuna Ahuja, Karan Sikka, Anirban Roy, Ajay Divakaran

We show that our model outperforms other baselines on the benchmark Ad dataset and also show qualitative results to highlight the advantages of using multihop co-attention.

Zero-Shot Object Detection

no code implementations ECCV 2018 Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, Ajay Divakaran

We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training.

object-detection Zero-Shot Learning +1

Human Social Interaction Modeling Using Temporal Deep Networks

no code implementations6 May 2015 Mohamed R. Amer, Behjat Siddiquie, Amir Tamrakar, David A. Salter, Brian Lande, Darius Mehri, Ajay Divakaran

We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment.

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