classification
Classification is a supervised learning task where the objective is to assign a category or label to each input data point based on learned features. It is a fundamental problem within machine learning.
- A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
- Balanced Active Inference
- Can Class-Priors Help Single-Positive Multi-Label Learning?
- Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
- Differentially Private High-dimensional Variable Selection via Integer Programming
- Disentangled Cross-Modal Representation Learning with Enhanced Mutual Supervision
- Harnessing the Universal Geometry of Embeddings
- HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell data
- How Classifier Features Transfer to Downstream: An Asymptotic Analysis in a Two-Layer Model
- Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
- Learning Task-Agnostic Representations through Multi-Teacher Distillation
- MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?
- Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks
- Massive Sound Embedding Benchmark (MSEB)
- One Sample is Enough to Make Conformal Prediction Robust
- STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
- Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
- TopER: Topological Embeddings in Graph Representation Learning
- Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution