Country
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
Recently, deep models have achieved great success in time series forecasting.
Structuring UncertaintyforFine-GrainedSampling inStochasticSegmentationNetworks
Weobtain them directly from the low-rank Gaussian distribution for the logits in the network head of SSNs, based on a previously unconsidered view of this distribution as a factor model.
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, Rohan Kadekodi
Neural Information Processing Systems http://nips.cc/
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Recently, large language models (LLMs) ( e.g., GPT -3 and ChatGPT) have shown remarkable zero-shot and few-shot performance on various tasks [
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, zhifeng Chen
Inmany cases, increasing model capacity beyond the memory limit of a single acceleratorhas required developing special algorithms orinfrastructure. These solutions are often architecture-specific and do not transfer to other tasks.
ImplicitDeepAdaptiveDesign: Policy-Based ExperimentalDesignwithoutLikelihoods
For example, suppose that a chemical contaminant has accidentally been released and is rapidly spreading; we need to quickly discover its unknown source.
b1656d20067ca7c84a33785c4083a75e-Paper-Conference.pdf
To demonstrate the utility ofPREF-SHAP, we apply our method to a variety of synthetic and real-worlddatasets andshowthatricher andmoreinsightful explanations canbe obtainedoverthebaseline.