Education
Pessimistic Backward Policy for GFlowNets
To this end, GFlowNets construct an object through a trajectory of state transitions, e.g., iteratively adding molecular fragments to construct a molecule.
NetworkGym: Reinforcement Learning Environments
We make use of four internal 12 GB NVIDIA TIT AN Xp GPUs to perform our experiments. At initialization of each environment, four UEs are randomly stationed 1.5 meters above the The L TE base station lies at ( x, z) = (40 m, 3m) . We use random seed values from 0 to 63, inclusive, for this parameter. Do not distribute. of four We train PTD3 for 10,000 steps, instead of 1,000,000 steps, which we do for TD3+BC.
Neuronal Competition Groups with Supervised STDP for Spike-Based Classification
Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backprop-agation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WT A) competition to learn distinct patterns.
Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding
We present a novel OCR-free document understanding framework based on pre-trained Multimodal Large Language Models (MLLMs).
Lean Workbook: A large-scale Lean problem set formalized from natural language math problems
I do believe that problems are the heart of mathematics.
Self-Labeling the Job Shop Scheduling Problem
An obstacle in applying supervised paradigms to such problems is the need for costly target solutions often produced with exact solvers.
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models Hannah Rose Kirk 1
Human feedback is central to the alignment of Large Language Models (LLMs).
TAIA: Large Language Models are Out-of-Distribution Data Learners
However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downstream distribution.
