8 citations · 9 across the 12 of their papers we have counts for
12 papers
I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
We introduce I-RAVEN-X, a symbolic benchmark designed to evaluate generalization and robustness in analogical and mathematical reasoning for Large Language Models (LLMs) and Large…
A foundation model with multi-variate parallel attention to generate neuronal activity
Francesco Carzaniga, Michael Hersche, Abu Sebastian +2
Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, particularly in clinical domains such as…
Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established…
Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning
Michael Hersche, Giacomo Camposampiero, Roger Wattenhofer +2
This work compares large language models (LLMs) and neuro-symbolic approaches in solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves the unde…
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Aleksandar Terzić, Michael Hersche, Giacomo Camposampiero +3
Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these mode…
On the Role of Noise in Factorizers for Disentangling Distributed Representations
Geethan Karunaratne, Michael Hersche, Abu Sebastian +1
To efficiently factorize high-dimensional distributed representations to the constituent atomic vectors, one can exploit the compute-in-superposition capabilities of vector-symboli…
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization
Gentiana Rashiti, Geethan Karunaratne, Mrinmaya Sachan +2
The retrieval augmented generation (RAG) system such as Retro has been shown to improve language modeling capabilities and reduce toxicity and hallucinations by retrieving from a d…
Towards Learning Abductive Reasoning using VSA Distributed Representations
Giacomo Camposampiero, Michael Hersche, Aleksandar Terzić +3
We introduce the Abductive Rule Learner with Context-awareness (ARLC), a model that solves abstract reasoning tasks based on Learn-VRF. ARLC features a novel and more broadly appli…
Zero-shot Classification using Hyperdimensional Computing
Samuele Ruffino, Geethan Karunaratne, Michael Hersche +3
Classification based on Zero-shot Learning (ZSL) is the ability of a model to classify inputs into novel classes on which the model has not previously seen any training examples. P…
Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
Michael Hersche, Francesco di Stefano, Thomas Hofmann +2
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently sol…
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
Aleksandar Terzic, Michael Hersche, Geethan Karunaratne +3
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as , with being the s…
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Nicolas Menet, Michael Hersche, Geethan Karunaratne +3
With the advent of deep learning, progressively larger neural networks have been designed to solve complex tasks. We take advantage of these capacity-rich models to lower the cost…