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 • 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 • 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 • 28 Mar 2022 • Xiaying Wang, Michael Hersche, Michele Magno, Luca Benini
A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement.
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 • 29 Sep 2021 • Rodolfo Octavio Siller Quintanilla, Xiaying Wang, Michael Hersche, Luca Benini, Gagandeep Singh
We propose new methods to induce denial-of-service attacks and incorporate domain-specific insights and constraints to accomplish two key goals: (i) create smooth adversarial attacks that are physiologically plausible; (ii) consider the realistic case where the attack happens at the origin of the signal acquisition and it propagates on the human head.
1 code implementation • 25 Mar 2021 • Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche, Alessio Burrello, Lukas Cavigelli, Luca Benini
With 9. 91 GMAC/s/W, it is 23. 0 times more energy-efficient and 46. 85 times faster than an implementation on the ARM Cortex M4F (0. 43 GMAC/s/W).
1 code implementation • 22 Feb 2021 • Xiaying Wang, Tibor Schneider, Michael Hersche, Lukas Cavigelli, Luca Benini
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action.
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.
1 code implementation • 31 May 2020 • Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini
Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77. 35% classification accuracy in 4-class MI.
1 code implementation • 24 Apr 2020 • Tibor Schneider, Xiaying Wang, Michael Hersche, Lukas Cavigelli, Luca Benini
We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0. 4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster.
no code implementations • 31 Mar 2020 • Xiaying Wang, Michael Hersche, Batuhan Tömekce, Burak Kaya, Michele Magno, Luca Benini
Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0. 31% with 7. 6x memory footprint reduction and a small accuracy loss of 2. 51% with 15x reduction.
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.
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.