Search Results for author:
Found 15 papers, 2 papers with code
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning
Our models were effective in both subtasks, with the best performance coming out of models with Effective Number of Samples (ENS) class weighting and token separated metadata in both subtasks.
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data
Due to the nature of the data captured by sensors that produce HSI images, a common issue is the dimensionality of the bands that may or may not contribute to the label class distinction.
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e. g., biomedical).
The Geometry of Multilingual Language Models: An Equality Lens
Understanding the representations of different languages in multilingual language models is essential for comprehending their cross-lingual properties, predicting their performance on downstream tasks, and identifying any biases across languages.
WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks
WADER uses data augmentation to address the problems of data imbalance and data scarcity and provides a method for data augmentation in cross-lingual, zero-shot tasks.
CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network
The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech, affecting people from various demographics.
A novel multimodal dynamic fusion network for disfluency detection in spoken utterances
Based on early-fusion and self-attention-based multimodal interaction between text and acoustic modalities, in this paper, we propose a novel multimodal architecture for disfluency detection from individual utterances.
Exploiting Nanoelectronic Properties of Memory Chips for Prevention of IC Counterfeiting
This study presents a methodology for anticounterfeiting of Non-Volatile Memory (NVM) chips.
Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study
Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications.
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse.
Low Power Neuromorphic EMG Gesture Classification
In this work, we address the aforementioned gaps with following key contributions: (1) Low-power, high accuracy demonstration of EMG-signal based gesture recognition using neuromorphic Recurrent Spiking Neural Networks (RSNN).
Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)
In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i. e. fused satellite images of warm-water lakes/marshy-lands or similar water-body sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks.
Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
In this paper, we present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing.
Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE Using OxRAM Crossbar
Post weight generation the OxRAM array is carefully programmed to binary weight-states using the proposed weight mapping technique on a custom-built testbench.
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures
To validate the proposed scheme we have simulated two different architectures: (i) Deep Belief Network (DBN) and (ii) Stacked Denoising Autoencoder for classification and reconstruction of hand-written digits from a reduced MNIST dataset of 6000 images.
