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Found 10 papers, 2 papers with code
Parametric Scaling Law of Tuning Bias in Conformal Prediction
In this work, we empirically find that the tuning bias - the coverage gap introduced by leveraging the same dataset for tuning and calibration, is negligible for simple parameter tuning in many conformal prediction methods.
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
While previous works have introduced several benchmarks to evaluate the safety risk of LLMs, the community still has a limited understanding of current LLMs' capability to recognize illegal and unsafe content in Chinese contexts.
Fine-tuning can Help Detect Pretraining Data from Large Language Models
In light of this, we introduce a novel and effective method termed Fine-tuned Score Deviation (FSD), which improves the performance of current scoring functions for pretraining data detection.
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model.
Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning.
TorchCP: A Python Library for Conformal Prediction
Conformal Prediction (CP) has attracted great attention from the research community due to its strict theoretical guarantees.
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
In this paper, we propose a straightforward, novel, and training-free hardness score named Distorting-based Learning Complexity (DLC), to identify informative images and instructions from the downstream dataset efficiently.
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects
During the instruction fine-tuning stage, we introduce semantic-aware visual feature extraction, a crucial method that enables the model to extract informative features from concrete visual objects.
Ranked #1 on
Image Captioning
on nocaps entire
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models.
Ranked #29 on
Image Super-Resolution
on Urban100 - 4x upscaling
Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs).
