8 citations · 10 across the 25 of their papers we have counts for
11 papers · 1 filter
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…
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…
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…
Terminating Differentiable Tree Experts
Jonathan Thomm, Michael Hersche, Giacomo Camposampiero +3
We advance the recently proposed neuro-symbolic Differentiable Tree Machine, which learns tree operations using a combination of transformers and Tensor Product Representations. We…
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…
12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson +4
Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forget…
Limits of Transformer Language Models on Learning to Compose Algorithms
Jonathan Thomm, Giacomo Camposampiero, Aleksandar Terzic +3
We analyze the capabilities of Transformer language models in learning compositional discrete tasks. To this end, we evaluate training LLaMA models and prompting GPT-4 and Gemini o…
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…