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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 5751–5800 of 9051 papers

TitleStatusHype
A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation SystemsCode0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots—0
Deep Gait Tracking With Inertial Measurement Unit—0
Should attention be all we need? The epistemic and ethical implications of unification in machine learning—0
Improving negation detection with negation-focused pre-training—0
A Closer Look at Few-shot Image Generation—0
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models—0
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature—0
Multi-Domain Targeted Sentiment Analysis—0
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation—0
Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms—0
Quantifying Synthesis and Fusion and their Impact on Machine Translation—0
Reconfigurable Heterogeneous Parallel Island Models—0
Phenomenology and dynamics of competitive ecosystems beyond the niche-neutral regimes—0
Power Scaling Law for Optical IRSs and Comparison with Optical Relays—0
Go Back in Time: Generating Flashbacks in Stories with Event Temporal PromptsCode0
An Analysis of Generative Methods for Multiple Image Inpainting—0
On the Diversity and Coded Modulation Design of Fluid Antenna Systems—0
Semantic Diversity in Dialogue with Natural Language Inference—0
Diverse Image Captioning with Grounded StyleCode0
Improving In-Context Few-Shot Learning via Self-Supervised Training—0
Seeding Diversity into AI Art—0
Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems—0
Studying Retrievability of Publications and Datasets in an Integrated Retrieval System—0
Ensemble pruning via an integer programming approach with diversity constraints—0
A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation—0
A Natural Diet: Towards Improving Naturalness of Machine Translation Output—0
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques—0
DMix: Adaptive Distance-aware Interpolative MixupCode0
NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM—0
MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM—0
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide—0
giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI—0
Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferencesCode0
SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers—0
SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model—0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments—0
LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection—0
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and InclusionCode0
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention MechanismCode0
Improving Personalized Explanation Generation through Visualization—0
Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English—0
IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning—0
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology—0
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding—0
IIT Dhanbad @LT-EDI-ACL2022- Hope Speech Detection for Equality, Diversity, and Inclusion—0
Sammaan@LT-EDI-ACL2022: Ensembled Transformers Against Homophobia and Transphobia—0
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists—0
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