Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
In-context learning and Occam's razor
Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie
MACK: Mismodeling Addressed with Contrastive Knowledge
Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Ernesto Arganda, Marcela Carena, Martín de los Rios, Andres D. Perez, Duncan Rocha, Rosa M. Sandá Seoane, Carlos E. M. Wagner
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Hagen Holthusen, Tim Brepols, Kevin Linka, Ellen Kuhl
Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment
Balaji Shesharao Ingole, Vishnu Ramineni, Nikhil Bangad, Koushik Kumar Ganeeb, Priyankkumar Patel
SSET: Swapping-Sliding Explanation for Time Series Classifiers in Affect Detection
Nazanin Fouladgar, Marjan Alirezaie, Kary Främling
Machine Learning Approach to Brain Tumor Detection and Classification
Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh
Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
Valerie Vaquet, Fabian Hinder, André Artelt, Inaam Ashraf, Janine Strotherm, Jonas Vaquet, Johannes Brinkrolf, Barbara Hammer
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining
Gustavo Assunção, Paulo Menezes
Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments
Al Amin, Kamrul Hasan, Saleh Zein-Sabatto, Liang Hong, Sachin Shetty, Imtiaz Ahmed, Tariqul Islam
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI
Arya Tschand (1), Arun Tejusve Raghunath Rajan (2), Sachin Idgunji (3), Anirban Ghosh (3), Jeremy Holleman (4), Csaba Kiraly (5), Pawan Ambalkar (6), Ritika Borkar (3), Ramesh Chukka (7), Trevor Cockrell (6), Oliver Curtis (8), Grigori Fursin (9), Miro Hodak (10), Hiwot Kassa (11), Anton Lokhmotov (12), Dejan Miskovic (3), Yuechao Pan (13), Manu Prasad Manmathan (7), Liz Raymond (6), Tom St. John (14), Arjun Suresh (15), Rowan Taubitz (8), Sean Zhan (8), Scott Wasson (16), David Kanter (16), Vijay Janapa Reddi (1) ((1) Harvard University, (2) Self / Meta, (3) NVIDIA, (4) UNC Charlotte / Syntiant, (5) Codex, (6) Dell, (7) Intel, (8) SMC, (9) FlexAI / cTuning, (10) AMD, (11) Meta, (12) KRAI, (13) Google, (14) Decompute, (15) GATE Overflow, (16) MLCommons)
Encoding architecture algebra
Stephane Bersier, Xinyi Chen-Lin
Machine Learning via rough mereology
Lech T. Polkowski
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models
Hossein Abdi, Mingfei Sun, Andi Zhang, Samuel Kaski, Wei Pan
Data Selection for Task-Specific Model Finetuning
Zifan Liu, Amin Karbasi, Theodoros Rekatsinas
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce D. Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni