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Predictive Attractor Models
The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,
NetHack is Hard to Hack
In this work, we conduct a comprehensive study of NetHack and examine various learning mechanisms to enhance the performance of neural models.
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao
Neural Information Processing Systems http://nips.cc/
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best
Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation
Empirically, the gradual self-training method greatly improves the target domain's accuracy compared
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
PHUONG_HA NGUYEN, Lam Nguyen, Marten van Dijk
Neural Information Processing Systems http://nips.cc/
Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro
Neural Information Processing Systems http://nips.cc/
5c7465e4a236b56226ca221f3d16bf2b-Paper-Conference.pdf
Text-to-Image (T2I) has witnessed significant advancements, demonstrating superior performance for various generative tasks.