Genre
Benchmark Data Repositories for Better Benchmarking
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities.
Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms
Modern vision models are trained on very large noisy datasets.
Diffusion Schrรถdinger Bridge Matching
Solving transport problems, i.e. finding a map transporting one given distribution
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
This algorithm progressively decreases the length scale, expanding the class of functions considered by the optimizer.
Supplementary Material A Access to and Benchmark
Figure 10: Illustration of the frame-based pupil segmentation: (a) the input eye image I; (b) the generate binary mask M; and (c) the detected pupil boundary Q and the pupil center c. 16 C More Details in Experiment C.1 Evaluation metrics The detailed description of the four metrics adopted for the dataset evalution are as follows:
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling.