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Search · four archives
27 papers · ranked by Valyu relevance
Tien Thanh Thach, Antonio M. Scarfone
Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages…
Joseph G. Makin, David A. Moses, Edward F. Chang
A decade after the first successful attempt to decode speech directly from human brain signals, accuracy and speed remain far below that of natural speech or typing. Here we show how to achieve high accuracy from the electrocorticogram at natural-speech rates, even with few data (on the order of half an hour of spoken…
Siwei Wang, Benjamin Hoshal, Elizabeth A de Laittre, Olivier Marre + 2 more
Much of sensory neuroscience focuses on presenting stimuli that are chosen by the experimenter because they are parametric and easy to sample and are thought to be behaviorally relevant to the organism. However, it is not generally known what these relevant features are in complex, natural scenes. This work focuses on…
Kaeser M. Sabrin, Yongbin Wei, Martijn van den Heuvel, Constantine Dovrolis
We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode’s muscles. We…
Julian D. Parker, A. Yu. Smirnov, Jordi Pons, CJ Carr + 3 more
The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Traditionally such tokenization models have concentrated on low parameter-count architectures using only components with strong inductive…
Ahmed A. Harby, Farhana Zulkernine, Hanady M. Abdulsalam
The rapid growth of multimedia content has increased the demand for effective methods to reduce storage requirements while maintaining quality and enabling fast data transmission. Existing standards and generative model approaches often involve high computational cost, require extensive parameter tuning, and produce…
Hatem Ibrahem, Ahmed Salem, Hyun-Soo Kang, Jiayi Ma
The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the image locally. ViTs outperform the convolutional neural networks in…
Andrew Garrett Kurbis, Alex Mihailidis, Brokoslaw Laschowski
Decoding algorithms can be used to predict motor behaviour from patterns of neural activity. However, most studies rely on subject-optimized models, limiting generalization and scalability to novel subjects and tasks. Building on recent advances in deep learning and large-scale data, here we developed an EMG foundation…
Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for…
Authors not listed
Deep generative models are transforming early-stage drug discovery, yet most current approaches are not well suited for realistic, small-data settings and often rely on simplified molecular representations such as linear strings, overlooking the inherent graph-based structure of molecules. To address this, we first…
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten + 3 more
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal. Here we develop a new…
Jianhua Hu, Guixiang Luo, Xiangfei Feng, Zhanjiang Yuan + 3 more
Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well…
Yang Yang, Guillaume Sautière, Je-Hwan Ryu, Taco Cohen
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete…
Mohaimen Mohammed, Mesut Çevik, Stefano Savazzi
This paper presents a Deep Autoencoder-LDPC-OFDM (DAE-LDPC-OFDM) transceiver architecture that integrates a learned belief propagation (BP) decoder to achieve robust, energy-efficient, and adaptive wireless communication. Unlike conventional modular systems that treat encoding, modulation, and decoding as independent…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…
Jannis Clausius, Marvin Geiselhart, Stephan ten Brink
—For improving short-length codes, we demonstrate that classic decoders can also be used with real-valued, neural encoders, i.e., deep-learning based "codeword" sequence generators. Here, the classical decoder can be a valuable tool to gain insights into these neural codes and shed light on weaknesses. Specifically…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Chen Fu, Heming Sun, Zhiqiang Zhang, Jinjia Zhou + 1 more
Recently, specifically designed video codecs have been preferred due to the expansion of video data in Internet of Things (IoT) devices. Context Adaptive Binary Arithmetic Coding (CABAC) is the entropy coding module widely used in recent video coding standards such as HEVC/H.265 and VVC/H.266. CABAC is a well known…
Jannis Clausius, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink
'Stephan ten Brink'] Abstract—Attracted by its scalability towards practical codeword lengths, we revisit the idea of Turbo-autoencoders for endto-end learning of PHY-Layer communications. For this, we study the existing concepts of Turbo-autoencoders from the literature and compare the concept with state-of-the-art…
Pieter Floris Jacobs, Robert Pollice
Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise.…
Chin Wa Lau, Xiang Shi, Zheng, Ziyan + 3 more
—Transformer-based neural decoders have emerged as a promising approach to error correction coding, combining data-driven adaptability with efficient modeling of long-range dependencies. This paper presents a novel decoder architecture that integrates classical belief propagation principles with transformer designs. We…
Eren Balevi, Jeffrey G. Andrews
—This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction code for intractable nonlinear channels. The LDPC encoder shrinks the input space of the…
Arthur Petrosyan, Mikhail Lebedev, Alexei Ossadtchi
Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. In recent years, we have seen an emergence of new algorithms for BCI decoding. Here we propose a compact architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Atlas Kazemian, Eric Elmoznino, Michael F. Bonner
What underlies the emergence of cortex-aligned representations in deep neural network models of vision? We examined networks with varied architectures but no pre-training and quantified their ability to predict image representations in the visual cortices of both monkeys and humans. We found that cortex-aligned…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…