Search Results for author:
Found 10 papers, 6 papers with code
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
In this paper, we introduce a new variant of crowd counting problem, namely "Categorized Crowd Counting", that counts the number of people sitting and standing in a given image.
Boosting House Price Predictions using Geo-Spatial Network Embedding
Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities.
A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations
Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.
Ranked #7 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations.
Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications
In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications.
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format.
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
