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Found 14 papers, 4 papers with code
LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers
Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement.
LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey
Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks.
Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN
In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework.
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy.
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
Generic text summarization approaches often fail to address the specific intent and needs of individual users.
A Dataset of Inertial Measurement Units for Handwritten English Alphabets
This paper presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style.
Modeling and Quickest Detection of a Rapidly Approaching Object
This change in distribution could be due to an event or a sudden arrival of an enemy object.
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation
During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure.
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning
In this paper, we propose a federated learning approach to suppress the unequal distribution of the noisy labels in the dataset of each participant.
Generating Fact Checking Summaries for Web Claims
SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim.
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching
On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness.
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions
In this paper, we present a comprehensive review of existing literature on compressing DNN model that reduces both storage and computation requirements.
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
