arXiv research

Proposes intelligent web browser features for link prediction and category-wise recommendation.

Federated Learning improves Firefox URL bar suggestions.

Web browser autofill predicts form field labels for convenience.

Model predicts web page parallelism for improved browser performance and energy.

Percival uses deep learning to block ads in real-time, minimizing performance impact.

Interactive tools help teach economics online.

Extracts main content from web pages using neural sequence labeling.

Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allow…

ImJoy simplifies deep learning for biomedical research.

AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence o…

With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticate…

Semi-supervised model removes noisy content from webpages.

Masked LARk prevents cross-site tracking while training models.

Bayesian inference over admissible histories leads to irreversible kinetics.

HS-FNO models non-Markovian PDEs by learning history and future states.

Overview of affine surface area and its history.

Survey of Baum-Connes conjecture history and methods.

Survey on DDVV-type inequalities, their history, and recent developments.

SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.

Paper analyzes history-based RL methods for MDPs, introduces a theoretical framework and practical algorithm.

Neural networks improve cancer risk prediction from family history data.

The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.

New algorithm minimizes regret in stochastic linear bandits with perturbed history.

It is shown how the generating functional method of De Dominicis can be used to solve the dynamics of the original version of the minority game (MG), in which agents observe real as opposed to fake market histories. Here one again finds exact closed equations for correlation and response functions, but now these are de…

This paper suggests claim history will be deprecated in future auto insurance rates.

We present in this chapter (Chapter II) the history of ideas which lead up to the development of modern knot theory. We are more detailed when pre-XX century history is reported. With more recent times we are more selective, stressing developments related to Jones type invariants of links. In the Appendix, A.Przybyszew…

PHE adds pseudo-rewards to history to minimize regret in stochastic bandits.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript.…

DR-FRL learns functional states from irregular histories for causal inference.

Paper tackles reinforcement learning for STL specifications with state history.

We give an explicit definition of decentralization and show you that decentralization is almost impossible for the current stage and Bitcoin is the first truly noncentralized currency in the currency history. We propose a new framework of noncentralized cryptocurrency system with an assumption of the existence of a wea…

Cold-start PV forecasting uses synthetic histories to train time-series foundation models.

Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…

Combining deep learning and ensemble smoothers for better history matching.

New framework handles dynamic contexts in reinforcement learning.

A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.

Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.

Foundation models improve wage gap decomposition by capturing omitted career history factors.

Marden's Tameness Conjecture predicts that every hyperbolic 3-manifold with finitely generated fundamental group is homeomorphic to the interior of a compact 3-manifold. It was recently established by Agol and Calegari-Gabai. We will survey the history of work on this conjecture and discuss its many applications.

Bäcklund transformations for smooth and ``space discrete'' Hashimoto surfaces are discussed and a geometric interpretation is given. It is shown that the complex curvature of a discrete space curve evolves with the discrete nonlinear Schrödinger equation (NLSE) of Ablowitz and Ladik, when the curve evolves with the Has…

Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we in…

Deep learning calibrates CO2 storage formations from seismic and well data.

Saec compresses recommendation system embeddings by clustering similar features.

A new method uses a frozen language model to improve sample efficiency in reinforcement learning.

HDT improves MCMC on graphs with history-dependent sampling.

New scheme adapts batch size for faster variance-reduced algorithms.

Paper presents a method for recognizing human actions using GLAC features from motion and static images.

Rhino learns causal relationships from time series data with history-dependent noise.