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Found 28 papers, 6 papers with code
Efficient Decoding of Compositional Structure in Holistic Representations
In particular, we find that the decoding techniques from the sparse coding and compressed sensing literature (rarely used for Hyperdimensional Computing/Vector Symbolic Architectures) are also well-suited for decoding information from the compositional distributed representations.
Efficient Optimization with Higher-Order Ising Machines
A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i. e., hardware implementations of networks of interacting binary spin variables.
Computing with Hypervectors for Efficient Speaker Identification
With only 1. 02k active parameters and a 128-minute pass through the training data we achieve Top-1 and Top-5 scores of 31% and 52% on the VoxCeleb1 dataset of 1, 251 speakers.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
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.
Integer Factorization with Compositional Distributed Representations
In this paper, we present an approach to integer factorization using distributed representations formed with Vector Symbolic Architectures.
Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences
Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
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.
Hyperseed: Unsupervised Learning with Vector Symbolic Architectures
Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of Vector Symbolic Architectures (VSA) for fast learning of a topology preserving feature map of unlabelled data.
Computing on Functions Using Randomized Vector Representations
By analogy to VSA, we call this new function encoding and computing framework Vector Function Architecture (VFA).
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements.
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary training ones.
Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware
This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing).
Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks
In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less computationally extensive training and inference approaches.
Perceptron Theory for Predicting the Accuracy of Neural Networks
The first two moments of the distribution of the postsynaptic sums can predict the overall network performance quite accurately.
End to End Binarized Neural Networks for Text Classification
In this paper, we propose an end to end binarized neural network architecture for the intent classification task.
Cellular Automata Can Reduce Memory Requirements of Collective-State Computing
The variable encoding uses a fixed set of random patterns, which has to be stored and kept available during the computation.
Variable Binding for Sparse Distributed Representations: Theory and Applications
One binding method for general sparse vectors extends earlier proposals to reduce the tensor product into a vector, such as circular convolution.
Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10
This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1].
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics
The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e. g., for one of the classifiers on a small dataset, the memory reduction was 6. 18 times; while train and test speed-ups were 4. 62 and 3. 84 times, respectively.
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view.
A theory of sequence indexing and working memory in recurrent neural networks
The theory describes linear readout of analog data, and readout with winner-take-all error correction of symbolic data as proposed in VSA models.
Theory of the superposition principle for randomized connectionist representations in neural networks
The superposition operations in different existing models are mapped to linear neural networks with unitary recurrent matrices, in which retrieval accuracy can be analyzed by a single equation.
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware
We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing.
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
Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli.
