23 papers · ranked by Valyu relevance
Defu Liu, Yixiao Zhu, Zhe Liu, Yi Liu + 4 more
'Ruihao Li' 'Wei Yi'] The exceptional performance of general-purpose large models has driven various industries to focus on developing domain-specific models. However, large models are not only time-consuming and labor-intensive during the training phase but also have very high hardware requirements during the…
Moshik Hershcovitch, Andrew W. Wood, Leshem Choshen, Guy Girmonsky + 6 more
'Roy Leibovitz' 'Ilias Ennmouri' 'Michal Malka' 'Peter Chin' 'Sundararaman Swaminathan' 'Danny Harnik'] With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression…
Jasmine Quah, Omer Sella, Thomas Heinis
DNA is a leading candidate as the next archival storage media due to its density, durability and sustainability. To read (and write) data DNA storage exploits technology that has been developed over decades to sequence naturally occurring DNA in the life sciences. To achieve higher accuracy for previously unseen…
Angie Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren + 2 more
Across ML Model Compression Experiments Authors: Angie Boggust, Venkatesh Sivaraman, Yannick Assogba, Donghao Ren, Dominik Moritz, Fred Hohman Title: Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Authors: Angie Boggust, Venkatesh Sivaraman, Yannick…
Fatemeh Kamali, Amir Abolfazl Suratgar, Mohammadbagher Menhaj, Reza Abbasi-Asl
Voxelwise encoding models based on convolutional neural networks (CNNs) have emerged as state-of-the-art predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models have made them difficult to interpret. Here, we…
Moshik Hershcovitch, Leshem Choshen, Andrew W. Wood, Ilias Enmouri + 3 more
'Peter Chin' 'Sundararaman Swaminathan' 'Danny Harnik'] Abstract—With the growth of model sizes and scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast literature about reducing model sizes, we investigate a more…
Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangmin Liu + 1 more
Framework Authors: ['Boyang Zhang' 'Daning Cheng' 'Yunquan Zhang' 'Fangmin Liu' 'Wenguang Chen'] Boyang Zhang1,2 , Daning Cheng1 , Yunquan Zhang1,2 , Fangmin Liu2 , Wenguang Chen2,3 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China…
Samir Brahim Belhaouari, Insaf Kraidia
Large Language Models (LLMs) have revolutionized artificial intelligence by enabling multitasking across diverse fields. However, their high computational demands result in significant environmental impacts, particularly in terms of energy and water consumption. This paper addresses these issues by proposing an…
Fatemeh Kamali, Amir Abolfazl Suratgar, Mohammadbagher Menhaj, Reza Abbasi-Asl + 1 more
'Reza Abbasi-Asl' 'Daniele Marinazzo'] Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models have made them difficult to…
Marek Kokot, Amitava Roy, Travis J Wheeler, Sebastian Deorowicz
Molecular dynamics (MD) simulations model the physical movements of atoms in biomolecular systems over time, providing atomic-resolution insight into conformational changes, binding events, and dynamic behaviors that cannot be captured by static structures alone. As such, MD simulations are playing an increasingly…
Zhimin Li, Harshitha Menon, Charles Jekel, Valerio Pascucci + 1 more
Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace time-consuming numerical simulations with learned alternatives, providing quick solutions. However, high-fidelity generative surrogate…
Amy X. Lu, Wilson Yan, Kevin K. Yang, Vladimir Gligorijevic + 4 more
Existing protein machine learning representations typically model either the sequence or structure distribution, with the other modality implicit. The latent space of sequence-to-structure prediction models such as ESMFold represents the joint distribution of sequence and structure; however, we find these embeddings to…
Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Anan Li + 7 more
Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning based methods demand huge training data and are…
Emir Öztürk, Altan Mesut, Stefano Cirillo
Learning-based data compression methods have gained significant attention in recent years. Although these methods achieve higher compression ratios compared to traditional techniques, their slow processing times make them less suitable for compressing large datasets, and they are generally more effective for short…
Swathi Shree Narashiman, Nitin Chandrachoodan
Data compression continues to evolve, with traditional information theory methods being widely used for compressing text, images, and videos. Recently, there has been growing interest in leveraging Generative AI for predictive compression techniques. This paper 1 introduces a lossless text compression approach using a…
Felix Zeller, Chieh-Min Hsieh, Wilke Dononelli, Tim Neudecker
The field of liquid-phase and solid-state high-pressure chemistry has exploded since the advent of the diamond anvil cell, an experimental technique that allows the application of pressures up to several hundred gigapascal. To complement high-pressure experiments, a large number of computational tools have been…
Sibusiso B. Buthelezi, Jules R. Tapamo, Nikolaos Mitianoudis
We present a hybrid end-to-end learned image compression framework that combines a CNN-based variational autoencoder (VAE) with an efficient hierarchical Swin Transformer to address the limitations of existing entropy models in capturing global dependencies under computational constraints. Traditional VAE-based codecs…
Anders Andreasen, Maria Bonto, Fernando Montero
– This paper presents a framework for optimisation and techno-economic analysis of various pressurisation pathways for CO2 pipeline transportation. The pressurisation pathways include a conventional compression only case from initial to final pressure, a sub-critical compression part followed by cooling, liquefaction…
Daniel Probst
Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecules are often represented as strings, such as SMILES for small…
Mathias Peirlinck, Kevin Linka, Juan A. Hurtado, Gerhard A. Holzapfel + 1 more
Personalized computational simulations have emerged as a vital tool to understand the biomechanical factors of a disease, predict disease progression, and design personalized intervention. Material modeling is critical for realistic biomedical simulations, and poor model selection can have life-threatening consequences…
David Podgorelec, Damjan Strnad, Ivana Kolingerová, Borut Žalik + 1 more
'Jun Chen'] After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others…
Authors not listed
Cost effective and reliable hydrogen compression remains a challenging barrier in the wide-spread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by non-mechanical compression strategies (e.g., electrochemical…
Authors not listed
Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but is challenging due to the large number of reactions occurring at various time scales. Here, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic…