31 citations · 74 across the 15 of their papers we have counts for
17 papers
Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models
Yuhang Song, Bor-Jiun Lin, Jiaxu Liu +3
Historical context integration presents a fundamental challenge for Vision-Language Models (VLMs) in sequential decision-making tasks. Current VLMs process visual inputs independen…
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Viet Dung Nguyen, Yuhang Song, Anh Nguyen +3
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high coll…
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
Lingdong Kong, Shaoyuan Xie, Zeying Gong +135
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…
Cisco Time Series Model Technical Report
Liang Gou, Archit Khare, Praneet Pabolu +10
We introduce the Cisco Time Series Model, a univariate zero-shot forecaster. This time series foundation model is the result of a general architectural innovation to a time series…
A Parameter-Efficient Mixture-of-Experts Framework for Cross-Modal Geo-Localization
LinFeng Li, Jian Zhao, Zepeng Yang +6
We present a winning solution to RoboSense 2025 Track 4: Cross-Modal Drone Navigation. The task retrieves the most relevant geo-referenced image from a large multi-platform corpus…
Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization
Yuhang Song, Mario Gianni, Chenguang Yang +4
This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural languag…
Associative Memories in the Feature Space
Tommaso Salvatori, Beren Millidge, Yuhang Song +2
An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. H…
Learning to Terminate in Object Navigation
Yuhang Song, Anh Nguyen, Chun-Yi Lee
This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in…
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Tommaso Salvatori, Yuhang Song, Yordan Yordanov +6
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstabl…
Predictive Coding beyond Gaussian Distributions
Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov +3
A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP). A prominent…
Bird-Eye Transformers for Text Generation Models
Lei Sha, Yuhang Song, Yordan Yordanov +2
Transformers have become an indispensable module for text generation models since their great success in machine translation. Previous works attribute the~success of transformers t…
A Theoretical Framework for Inference and Learning in Predictive Coding Networks
Beren Millidge, Yuhang Song, Tommaso Salvatori +2
Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process o…
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning
Beren Millidge, Yuhang Song, Tommaso Salvatori +2
How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients tha…
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge, Tommaso Salvatori, Yuhang Song +2
A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories…
Learning on Arbitrary Graph Topologies via Predictive Coding
Tommaso Salvatori, Luca Pinchetti, Beren Millidge +4
Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagate…
Associative Memories via Predictive Coding
Tommaso Salvatori, Yuhang Song, Yujian Hong +5
Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in…
Reverse Differentiation via Predictive Coding
Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz +2
Deep learning has redefined the field of artificial intelligence (AI) thanks to the rise of artificial neural networks, which are architectures inspired by their neurological count…