21 citations · 37 across the 8 of their papers we have counts for
16 papers
On the Role of Noise in Factorizers for Disentangling Distributed Representations
Geethan Karunaratne, Michael Hersche, Abu Sebastian +1
To efficiently factorize high-dimensional distributed representations to the constituent atomic vectors, one can exploit the compute-in-superposition capabilities of vector-symboli…
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization
Gentiana Rashiti, Geethan Karunaratne, Mrinmaya Sachan +2
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 d…
Zero-shot Classification using Hyperdimensional Computing
Samuele Ruffino, Geethan Karunaratne, Michael Hersche +3
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. P…
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
Aleksandar Terzic, Michael Hersche, Geethan Karunaratne +3
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as , with being the s…
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Nicolas Menet, Michael Hersche, Geethan Karunaratne +3
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…
Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing
Robert Guirado, Abbas Rahimi, Geethan Karunaratne +3
Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hypervectors). Among diff…