10 citations · 13 across the 6 of their papers we have counts for
7 papers
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito +4
The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmar…
Efficient Learning of Discrete-Continuous Computation Graphs
David Friede, Mathias Niepert
Numerous models for supervised and reinforcement learning benefit from combinations of discrete and continuous model components. End-to-end learnable discrete-continuous models are…
Learning Disentangled Discrete Representations
David Friede, Christian Reimers, Heiner Stuckenschmidt +1
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations…
Neural Architecture Performance Prediction Using Graph Neural Networks
Jovita Lukasik, David Friede, Heiner Stuckenschmidt +1
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. Due to the high computational co…
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
Jovita Lukasik, David Friede, Arber Zela +2
Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the…
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
David Friede, Jovita Lukasik, Heiner Stuckenschmidt +1
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. In the past, NAS was hardly acce…