1 code implementation • 12 Mar 2024 • Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson, Michael Hersche, Leo Zhao, Abbas Rahimi, Luca Benini
In this work, we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a lightweight model consisting of a pretrained and metalearned feature extractor and an expandable explicit memory storing the class prototypes.
no code implementations • 8 Feb 2024 • Jonathan Thomm, Aleksandar Terzic, Geethan Karunaratne, Giacomo Camposampiero, Bernhard Schölkopf, Abbas Rahimi
We analyze the capabilities of Transformer language models on learning discrete algorithms.
no code implementations • 30 Jan 2024 • Samuele Ruffino, Geethan Karunaratne, Michael Hersche, Luca Benini, Abu Sebastian, Abbas Rahimi
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.
Ranked #3 on Zero-Shot Learning on CUB-200-2011
1 code implementation • 29 Jan 2024 • Michael Hersche, Francesco Di Stefano, Thomas Hofmann, Abu Sebastian, Abbas Rahimi
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge.
no code implementations • 9 Dec 2023 • Aleksandar Terzic, Michael Hersche, Geethan Karunaratne, Luca Benini, Abu Sebastian, Abbas Rahimi
We build upon their approach by replacing the linear recurrence with a special temporal convolutional network which permits larger receptive field size with shallower networks, and reduces the computational complexity to $O(L)$.
1 code implementation • NeurIPS 2023 • Nicolas Menet, Michael Hersche, Geethan Karunaratne, Luca Benini, Abu Sebastian, Abbas Rahimi
MIMONets augment various deep neural network architectures with variable binding mechanisms to represent an arbitrary number of inputs in a compositional data structure via fixed-width distributed representations.
no code implementations • 10 Jul 2023 • Simon Raedler, Luca Berardinelli, Karolin Winter, Abbas Rahimi, Stefanie Rinderle-Ma
Each primary study will be evaluated and discussed with respect to the adoption of MDE principles and practices and the phases of AI development support aligned with the stages of the CRISP-DM methodology.
no code implementations • 24 Mar 2023 • Michael Hersche, Aleksandar Terzic, Geethan Karunaratne, Jovin Langenegger, Angéline Pouget, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi
We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function.
1 code implementation • 9 Nov 2022 • Jovin Langenegger, Geethan Karunaratne, Michael Hersche, Luca Benini, Abu Sebastian, Abbas Rahimi
Disentanglement of constituent factors of a sensory signal is central to perception and cognition and hence is a critical task for future artificial intelligence systems.
no code implementations • 14 Jul 2022 • Geethan Karunaratne, Michael Hersche, Jovin Langenegger, Giovanni Cherubini, Manuel Le Gallo-Bourdeau, Urs Egger, Kevin Brew, Sam Choi, INJO OK, Mary Claire Silvestre, Ning li, Nicole Saulnier, Victor Chan, Ishtiaq Ahsan, Vijay Narayanan, Luca Benini, Abu Sebastian, Abbas Rahimi
We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM).
2 code implementations • CVPR 2022 • Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi
Moreover, it is imperative that such learning must respect certain memory and computational constraints such as (i) training samples are limited to only a few per class, (ii) the computational cost of learning a novel class remains constant, and (iii) the memory footprint of the model grows at most linearly with the number of classes observed.
Ranked #4 on Few-Shot Class-Incremental Learning on mini-Imagenet
continual few-shot learning Few-Shot Class-Incremental Learning +1
no code implementations • 11 Mar 2022 • Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey, Abu Sebastian, Abbas Rahimi
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory.
1 code implementation • 9 Mar 2022 • Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, Abbas Rahimi
Compared to state-of-the-art deep neural network and neuro-symbolic approaches, end-to-end training of NVSA achieves a new record of 87. 7% average accuracy in RAVEN, and 88. 1% in I-RAVEN datasets.
no code implementations • 12 Nov 2021 • Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi
This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA).
no code implementations • 11 Nov 2021 • Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi
Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations.
no code implementations • 9 Jun 2021 • Denis Kleyko, Mike Davies, E. Paxon Frady, Pentti Kanerva, Spencer J. Kent, Bruno A. Olshausen, Evgeny Osipov, Jan M. Rabaey, Dmitri A. Rachkovskij, Abbas Rahimi, Friedrich T. Sommer
We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware.
no code implementations • 4 Feb 2021 • Manuel Eggimann, Abbas Rahimi, Luca Benini
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based on high-dimensional holistic representations of vectors.
no code implementations • 14 Oct 2020 • Michael Hersche, Luca Benini, Abbas Rahimi
Our first method, based on sparse bipolar random projection, projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too.
no code implementations • 5 Oct 2020 • Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data.
Few-Shot Image Classification Vocal Bursts Intensity Prediction
no code implementations • 4 Jun 2019 • Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abbas Rahimi, Abu Sebastian
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness.
no code implementations • 2 Jan 2019 • Ali Moin, Andy Zhou, Simone Benatti, Abbas Rahimi, Luca Benini, Jan M. Rabaey
Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition.
1 code implementation • 13 Dec 2018 • Michael Hersche, José del R. Millán, Luca Benini, Abbas Rahimi
All these methods, differing in complexity, aim to represent EEG signals in binary HD space, e. g. with 10, 000 bits.
no code implementations • 23 Nov 2018 • Abbas Rahimi, Tony F. Wu, Haitong Li, Jan M. Rabaey, H. -S. Philip Wong, Max M. Shulaker, Subhasish Mitra
By exploiting the unique properties of the underlying nanotechnologies, we show that HD computing, when implemented with monolithic 3D integration, can be up to 420X more energy-efficient while using 25X less area compared to traditional silicon CMOS implementations.
no code implementations • 6 Sep 2018 • Alessio Burrello, Kaspar Schindler, Luca Benini, Abbas Rahimi
This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG).
1 code implementation • 20 Jul 2018 • Manuel Schmuck, Luca Benini, Abbas Rahimi
In this paper, we propose hardware techniques for optimizations of HD computing, in a synthesizable VHDL library, to enable co-located implementation of both learning and classification tasks on only a small portion of Xilinx(R) UltraScale(TM) FPGAs: (1) We propose simple logical operations to rematerialize the hypervectors on the fly rather than loading them from memory.
2 code implementations • 18 Jun 2018 • Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone, Lukas Cavigelli, Luca Benini, Abbas Rahimi
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.
1 code implementation • 28 Feb 2018 • Ali Moin, Andy Zhou, Abbas Rahimi, Simone Benatti, Alisha Menon, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan, Fred Burghardt, Luca Benini, Ana C. Arias, Jan M. Rabaey
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
1 code implementation • 10 May 2017 • Denis Kleyko, Abbas Rahimi, Ross W. Gayler, Evgeny Osipov
A Bloom filter is a simple data structure supporting membership queries on a set.
Data Structures and Algorithms