Zobrazeno 1 - 10
of 359
pro vyhledávání: '"Rahimi, Abbas"'
Autor:
Büchel, Julian, Camposampiero, Giacomo, Vasilopoulos, Athanasios, Lammie, Corey, Gallo, Manuel Le, Rahimi, Abbas, Sebastian, Abu
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approximation in machine learning algorithms suitable for mixed-signal Analo
Externí odkaz:
http://arxiv.org/abs/2411.03375
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 database of non-parametric memory containing trillions of entries. We i
Externí odkaz:
http://arxiv.org/abs/2410.00004
Autor:
Thomm, Jonathan, Hersche, Michael, Camposampiero, Giacomo, Terzić, Aleksandar, Schölkopf, Bernhard, Rahimi, Abbas
We advance the recently proposed neuro-symbolic Differentiable Tree Machine, which learns tree operations using a combination of transformers and Tensor Product Representations. We investigate the architecture and propose two key components. We first
Externí odkaz:
http://arxiv.org/abs/2407.02060
Autor:
Camposampiero, Giacomo, Hersche, Michael, Terzić, Aleksandar, Wattenhofer, Roger, Sebastian, Abu, Rahimi, Abbas
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 applicable training objective for abductive reasoning, resulting in better
Externí odkaz:
http://arxiv.org/abs/2406.19121
Autor:
Wibowo, Yoga Esa, Cioflan, Cristian, Ingolfsson, Thorir Mar, Hersche, Michael, Zhao, Leo, Rahimi, Abbas, Benini, Luca
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 forgetting the previously learned classes. Classical backpropagation-based l
Externí odkaz:
http://arxiv.org/abs/2403.07851
Autor:
Thomm, Jonathan, Camposampiero, Giacomo, Terzic, Aleksandar, Hersche, Michael, Schölkopf, Bernhard, Rahimi, Abbas
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 on four tasks demanding to learn a composition of several discrete sub-
Externí odkaz:
http://arxiv.org/abs/2402.05785
Autor:
Ruffino, Samuele, Karunaratne, Geethan, Hersche, Michael, Benini, Luca, Sebastian, Abu, Rahimi, Abbas
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. Providing an auxiliary descriptor in the form of a set of attributes de
Externí odkaz:
http://arxiv.org/abs/2401.16876
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently solving Raven's progressive matrices (RPM), a visual test for assessing a
Externí odkaz:
http://arxiv.org/abs/2401.16024
Autor:
Terzic, Aleksandar, Hersche, Michael, Karunaratne, Geethan, Benini, Luca, Sebastian, Abu, Rahimi, Abbas
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as $O(LlogL)$, with $L$ being the sequence length. We build upon their approach by replacing the linear r
Externí odkaz:
http://arxiv.org/abs/2312.05605
Autor:
Menet, Nicolas, Hersche, Michael, Karunaratne, Geethan, Benini, Luca, Sebastian, Abu, Rahimi, Abbas
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 of inference by exploiting computation in superposition. To reduce the
Externí odkaz:
http://arxiv.org/abs/2312.02829