A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
ELM combines machine learning and feature engineering for anomalous diffusion detection.
TL-ANDI distills context from source data to improve transfer learning for TFMs.
Special issue on understanding physical processes from unusual diffusion patterns.
Bi-Mamba model predicts diffusion coefficients and exponents from short data.
The materials accompany a lecture short course presented at the 2011 Park City Mathematics Institute, Graduate Summer School on Moduli Spaces of Riemann Surfaces. The lectures were part of/coordinated with an overall program, including lectures by Ursula Hamenstadt on Teichmueller Theory, Andy Putman on Mapping Class a…
We prove a Simons-type holonomy theorem for totally skew 1-forms with values in a Lie algebra of linear isometries. The only transitive case, for this theorem, is the full orthogonal group. We only use geometric methods and we do not use any classification (not even that of transitive isometric actions on the sphere or…
New sampling scheme improves privacy in DP-SGD without sacrificing utility.
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provi…
NIST's CTS Challenge evaluates speaker recognition using telephony data.
Deep learning models are growing, posing new mathematical challenges.
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We pres…
Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.
ICLR 2021 challenge in computational geometry and topology attracted 16 teams.
Paper discusses ASD challenge for machine condition monitoring.
The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition dataset from YouTube videos together with ground truth annotation and standar…
XPDNet wins MRI reconstruction challenge with neural network.
One of the best ways for developers to test and improve their skills in a fun and challenging way are programming challenges, offered by a plethora of websites. For the inexperienced ones, some of the problems might appear too challenging, requiring some suggestions to implement a solution. On the other hand, tagging p…
Improved disentanglement through learned feature aggregation.
Challenge aims to develop automated meningioma MRI segmentation models.
Improved disentanglement in VAEs using aggregated feature maps.
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets w…
S2M optimizes mining for diverse data subpopulations.
Differentiable ABMs face challenges in inference and optimisation.
Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to…
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
Improved contact tracing models outperform NIST challenge results.
Bayesian optimization outperformed random search in machine learning hyperparameter tuning challenge.
This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and ph…
Machine learning simplifies finance, but faces challenges.
Quantum ML promises faster data analysis but faces trainability challenges.
NetML provides datasets and challenges for network traffic analysis.
Brief history and challenges of interpretable machine learning.
This review discusses challenges and solutions for AI in chemical engineering.
RL applied to finance tasks, highlighting challenges and future directions.
Researchers review challenges in interpreting additive models, especially neural additive models.
Bayesian design improves experimental optimization.
D2KLab's approach predicts tweet engagement using two stages.
This paper examines challenges in analyzing NFT transaction data.
Challenge forecasts EV charging station usage accurately.
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performanc…
AI enhances financial forecasting with challenges in regulation and privacy.
FedOS tackles challenges in federated learning by using open-set learning.
This is a report for reproducibility challenge of NeurlIPS 2019 on the paper Competitive Gradient Descent (Schafer et al., 2019). The paper introduces a novel algorithm for the numerical computation of Nash equilibria of competitive two-player games. It avoids oscillatory and divergent behaviours seen in alternating gr…
We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.
We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to c…
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal ident…
We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.
The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior t…