3 citations · 3 across the 9 of their papers we have counts for
10 papers
Temporal Recurrence Favors Fewer Layers
Ivan Anokhin, Johan Obando-Ceron, Irina Rish +1
In streaming tasks, recurrent models can carry latent computation across time, allowing each update to build on representations produced earlier. This raises a basic question: once…
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee +6
Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challeng…
Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions
Bingxu Liu, Jiashun Liu, Johan Obando-Ceron +5
While Proximal Policy Optimization (PPO) demonstrates strong performance in stationary settings, we show that its standard optimization paradigm struggles in continual and non-stat…
A Mechanistic Analysis of Looped Reasoning Language Models
Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron +4
Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an LLM's layers in the latent di…
Generating Creative Chess Puzzles
Xidong Feng, Vivek Veeriah, Marcus Chiam +10
While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to…
Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions
Vivek Veeriah, Federico Barbero, Marcus Chiam +10
The rapid advancement of Generative AI has raised significant questions regarding its ability to produce creative and novel outputs. Our recent work investigates this question with…
Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning
Jiashun Liu, Johan Obando-Ceron, Han Lu +7
Most recent RL for LLMs (RL4LLM) methods avoid explicit critics, replacing them with average advantage baselines. This shift is largely pragmatic: conventional value functions are…
Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion
Johan S. Obando-Ceron, Victor Romero-Cano, Sildomar Monteiro
Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances in the…
Divide and Conquer: an Accurate Machine Learning Algorithm to Process Split Videos on a Parallel Processing Infrastructure
Walter M. Mayor Toro, Juan C. Perafan Villota, Oscar H. Mondragon +1
Every day the number of traffic cameras in cities rapidly increase and huge amount of video data are generated. Parallel processing infrastruture, such as Hadoop, and programming m…
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
Johan S. Obando-Ceron, Victor Romero Cano, Walter Mayor Toro
This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It…