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Found 9 papers, 3 papers with code
Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research
This paper engages in a speculative exploration of the concept of an artificial agent capable of conducting research.
Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance
Research automation efforts usually employ AI as a tool to automate specific tasks within the research process.
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks.
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution.
Convergence of neural networks to Gaussian mixture distribution
We give a proof that, under relatively mild conditions, fully-connected feed-forward deep random neural networks converge to a Gaussian mixture distribution as only the width of the last hidden layer goes to infinity.
Image recognition via Vietoris-Rips complex
By considering this weighted graph as a pseudo-metric space, we construct a Vietoris-Rips complex with a parameter $\varepsilon$ by a well-known process of algebraic topology.
Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks
Teacher-student learning is a framework in which we introduce two neural networks: one neural network is a target function in supervised learning, and the other is a learning neural network.
Role of two learning rates in convergence of model-agnostic meta-learning
Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets
The local learning approach extracts semantic representations for these datasets by training the embedding model from scratch for each local neighborhood, respectively.
