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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 251–300 of 9051 papers

TitleStatusHype
EDGE: Editable Dance Generation From MusicCode2
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
Towards Building Text-To-Speech Systems for the Next Billion UsersCode2
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
BITS: Bi-level Imitation for Traffic SimulationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Classifier-Free Diffusion GuidanceCode2
CelebV-HQ: A Large-Scale Video Facial Attributes DatasetCode2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
Pretraining a Neural Network before Knowing Its ArchitectureCode2
Large Scale Radio Frequency Signal ClassificationCode2
Unsupervised Medical Image Translation with Adversarial Diffusion ModelsCode2
Semantic Image Synthesis via Diffusion ModelsCode2
BigBIO: A Framework for Data-Centric Biomedical Natural Language ProcessingCode2
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about ChangeCode2
SCAMPS: Synthetics for Camera Measurement of Physiological SignalsCode2
Text2Human: Text-Driven Controllable Human Image GenerationCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
MAT: Mask-Aware Transformer for Large Hole Image InpaintingCode2
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question AnsweringCode2
Stochastic Trajectory Prediction via Motion Indeterminacy DiffusionCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained EnvironmentsCode2
Freeform Body Motion Generation from SpeechCode2
A Contrastive Framework for Neural Text GenerationCode2
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
Accelerated Quality-Diversity through Massive ParallelismCode2
Trajectory balance: Improved credit assignment in GFlowNetsCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
ShapeFormer: Transformer-based Shape Completion via Sparse RepresentationCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty LearningCode2
Palette: Image-to-Image Diffusion ModelsCode2
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and TrackingCode2
Conditional GANs with Auxiliary Discriminative ClassifierCode2
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding TasksCode2
Diffusion Models Beat GANs on Image SynthesisCode2
BirdNET: A deep learning solution for avian diversity monitoringCode2
The Pile: An 800GB Dataset of Diverse Text for Language ModelingCode2
Improved StyleGAN Embedding: Where are the Good Latents?Code2
Multi-Objective Molecule Generation using Interpretable SubstructuresCode2
Scalability in Perception for Autonomous Driving: Waymo Open DatasetCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
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