Oceania
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination Xihuai Wang
The significant difference between the deployment-time partners' distribution and the training partners' distribution determined by the
The Morning After: Meta turned Threads algorithm complaints into an official feature
Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Dear algo posts on Threads will change your recommendations for three days. Threads users have complained about its recommendation algorithm since 2023. Users even started writing posts addressed to the algorithm, specifying the topics they wanted to see more of. Now, that's part of the system: Users can write a post that begins with "dear algo" to adjust their preferences, officially. For example, you could write: "Dear algo, show me more posts about sous vide recipes."
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
Amir Dezfouli, Richard Morris, Fabio T. Ramos, Peter Dayan, Bernard Balleine
Neural Information Processing Systems http://nips.cc/
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units
Neural Information Processing Systems http://nips.cc/
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
Learning to Teach with Dynamic Loss Functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
Theultimate goalofteacher model is cultivating the student to have better performance measured on development dataset.
Towards Deep Conversational Recommendations
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
Neural Information Processing Systems http://nips.cc/
A Stochastic Composite Gradient Method with Incremental Variance Reduction
Neural Information Processing Systems http://nips.cc/
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions.
Zero-Shot Tokenizer Transfer Benjamin Minixhofer
Our method comes close to the original models' performance in cross-lingual and