Search Results for author:
Found 6 papers, 5 papers with code
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks.
Efficient Learning of Discrete-Continuous Computation Graphs
We analyze the behavior of more complex stochastic computations graphs with multiple sequential discrete components.
Learning Disentangled Discrete Representations
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear.
Neural Architecture Performance Prediction Using Graph Neural Networks
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
We evaluate the proposed approach on neural architectures defined by the ENAS approach, the NAS-Bench-101 and the NAS-Bench-201 search space and show that our smooth embedding space allows to directly extrapolate the performance prediction to architectures outside the seen domain (e. g. with more operations).
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest.
