Search Results for author:
Found 17 papers, 6 papers with code
Dynamics and Computational Principles of Echo State Networks: A Mathematical Perspective
Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps from the state space.
From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection
This study explores the challenges of integrating human visual cue-based dehazing into object detection, given the selective nature of human perception.
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding
Known by more than 1. 5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures.
MoonMetaSync: Lunar Image Registration Analysis
This research contributes to computer vision and planetary science by comparing feature detection methods for lunar imagery and introducing a versatile tool for lunar image registration and evaluation, with implications for multi-resolution image analysis in space exploration applications.
Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions
The findings offer a viable solution for enhancing object detection in poor visibility and contribute to the broader understanding of integrating human visual principles into deep learning algorithms for intricate visual recognition challenges.
Moonshine: Distilling Game Content Generators into Steerable Generative Models
This study addresses these issues by distilling a constructive PCG algorithm into a controllable PCGML model.
WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding
In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety.
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
It assesses the complexity of tasks with the Hierarchical Prompting Index (HPI), which demonstrates the cognitive competencies of LLMs across diverse datasets and offers insights into the cognitive demands that datasets place on different LLMs.
Ranked #1 on
Machine Translation
on IWSLT 2017
A Methodology-Oriented Study of Catastrophic Forgetting in Incremental Deep Neural Networks
In such scenarios, the main concern is catastrophic forgetting(CF), i. e., while learning the sequentially, neural network underfits the old data when it confronted with new data.
The Ethics of Interaction: Mitigating Security Threats in LLMs
This paper comprehensively explores the ethical challenges arising from security threats to Large Language Models (LLMs).
Road Rutting Detection using Deep Learning on Images
The proposed road rutting dataset and the results of our research study will help accelerate the research on detection of road rutting using deep learning.
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks
While fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Transients generate memory and break hyperbolicity in stochastic enzymatic networks
We propose new statistical measures, defined in terms of turnover times, to distinguish between the transient and steady states and apply these to experimental data from a landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites.
Syntax-guided Controlled Generation of Paraphrases
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.
eCommerceGAN : A Generative Adversarial Network for E-commerce
Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecosystem, namely the customers and the products they purchase.
