Search Results for author:
Found 28 papers, 12 papers with code
12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
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.
Limits of Transformer Language Models on Learning Algorithmic Compositions
We analyze the capabilities of Transformer language models on learning discrete algorithms.
Zero-shot Classification using Hyperdimensional Computing
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
Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge.
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
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 $O(L)$.
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
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.
Model-Driven Engineering for Artificial Intelligence -- A Systematic Literature Review
Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art.
Factorizers for Distributed Sparse Block Codes
We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function.
In-memory factorization of holographic perceptual representations
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.
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
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).
Constrained Few-shot Class-incremental Learning
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
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
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.
A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices
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.
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
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).
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations
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.
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware
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.
A 5 μW Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based on high-dimensional holistic representations of vectors.
Binarization Methods for Motor-Imagery Brain-Computer Interface Classification
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.
Robust High-dimensional Memory-augmented Neural Networks
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
In-memory hyperdimensional computing
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.
Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing
Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition.
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
All these methods, differing in complexity, aim to represent EEG signals in binary HD space, e. g. with 10, 000 bits.
Hyperdimensional Computing Nanosystem
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.
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG).
Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory
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.
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
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.
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives
A Bloom filter is a simple data structure supporting membership queries on a set.
Data Structures and Algorithms
