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.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommen…
Meta-DRL improves resource allocation in O-RAN networks.
AI could democratize education but risks inequality.
This study improves the performance of neural named entity recognition by a margin of up to 11% in F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset. Rather than designing deeper and wider hy…
Paper proposes OPF policy for fair resource allocation with sublinear regret.
Federated Learning helps IoT devices learn without central servers.
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing re…
FinGPT is an open-source financial LLM for democratizing financial data.
Training machine learning (ML) models on large datasets requires considerable computing power. To speed up training, it is typical to distribute training across several machines, often with specialized hardware like GPUs or TPUs. Managing a distributed training job is complex and requires dealing with resource contenti…
HuSpaCy offers an industrial-grade Hungarian NLP toolkit.
Introduces challenges and techniques for creating machine translation for indigenous languages.
Uncertainty Toolbox aids in assessing and improving uncertainty quantification in machine learning.
Introduces AMLB, an open benchmark for AutoML frameworks.
This report has several purposes. First, our report is written to investigate the reproducibility of the submitted paper On the regularization of Wasserstein GANs (2018). Second, among the experiments performed in the submitted paper, five aspects were emphasized and reproduced: learning speed, stability, robustness ag…
DIGEN benchmark provides synthetic datasets for ML algorithm evaluation.
PyTorch adds tools for pruning neural networks.
Continuous-depth Evoformer reduces protein folding prediction time and resource usage.
Study shows multilingual LLM calibration effects improve model confidence but not accuracy.
Paper proposes decision-theoretic approach to combat wildfires.
Millimeter Wave (MmWave) communication is one of the key technology of the fifth generation (5G) wireless systems to achieve the expected 1000x data rate. With large bandwidth at mmWave band, the link capacity between users and base stations (BS) can be much higher compared to sub-6GHz wireless systems. Meanwhile, due …
Model for open, decentralized network with task load balancing.
We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be bel…
RAAL optimizes black box function optimization with multifidelity models.
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning me…
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networ…
New models improve morpheme segmentation in low-resource languages.
A new algorithm for resource-aware multi-armed bandits minimizes regret.
In recent years there has been a sharp rise in networking applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. Examples include analyzing network traffic under zero-day attacks, and computer vision tasks by sensor…
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can ex…
High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) l…
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model erro…
Optimal resource allocation in censored semi-bandits with unknown thresholds.
PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.
Method learns software resource usage from snapshots.
Proposes a new framework for resource-limited recommendation.
Introduces data ethics for mathematicians, covering background, open data, and privacy.
Deep neural networks (DNNs) have been employed for designing wireless systems in many aspects, say transceiver design, resource optimization, and information prediction. Existing works either use the fully-connected DNN or the DNNs with particular architectures developed in other domains. While generating labels for su…
Paper tackles pandemic resource allocation challenges.
We provide an online RLHF workflow for large language models.
New method detects anomalies in time series data, especially useful for monitoring services.
PASHA optimizes model tuning for large datasets with limited resources.
Study resource allocation strategies in sequential decisions with unknown rewards.
This paper presents an assessment of global economic energy potentials for all major natural energy resources. This work is based on both an extensive literature review and calculations using natural resource assessment data. Economic potentials are presented in the form of cost-supply curves, in terms of energy flows …
Cardea automates machine learning for EHRs, improving model building efficiency.
Handling the tremendous amount of network data, produced by the explosive growth of mobile traffic volume, is becoming of main priority to achieve desired performance targets efficiently. Opportunistic communication such as FloatingContent (FC), can be used to offload part of the cellular traffic volume to vehicular-to…
Generative profiling improves real-time task timing for varied resource contexts.