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.
Automated framework forecasts correlated time series in minutes.
Automated denoising score matching handles nonlinear diffusion processes.
Optimizes classifiers for varying levels of automation.
Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…
AI generates theorems and proofs for training theorem provers.
Painless Activation Steering automates post-training for LMs without manual intervention.
Abbreviation disambiguation is important for automated clinical note processing due to the frequent use of abbreviations in clinical settings. Current models for automated abbreviation disambiguation are restricted by the scarcity and imbalance of labeled training data, decreasing their generalizability to orthogonal s…
Automated suggestions help train technicians diagnose incidents faster.
Study examines how uncertainty visualization affects analyst trust in automated classification systems.
Automates neural network design without training, speeding up search by seconds.
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training or for training a newly synthesized model, one would not know the best learning rate regime beforehand. We propose an automated algorithm for…
Adversarial validation detects concept drift in user targeting systems.
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian an…
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
CausalPFN automates causal effect estimation from observational data.
In this work, we conducted a study on building an automated testing system for deep learning systems based on differential behavior criteria. The automated testing goals were achieved by jointly optimizing two objective functions: maximizing differential behaviors from models under testing and maximizing neuron coverag…
Statistical analysis (SA) is a complex process to deduce population properties from analysis of data. It usually takes a well-trained analyst to successfully perform SA, and it becomes extremely challenging to apply SA to big data applications. We propose to use deep neural networks to automate the SA process. In parti…
Unified scoring model improves efficiency and performance across multiple tasks.
Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.
Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria typ…
Automated detection of MS lesions improves to 67% with 7T MRI.
Automated Budget Constrained Training optimizes model training under time constraints.
Mango automates hyperparameter tuning for large-scale ML training.
The paper critiques UBI as ineffective for addressing technological unemployment.
Many continuous control tasks have easily formulated objectives, yet using them directly as a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many classical control tasks guide RL training using complex rewards, which require tedious hand-tuning. We automate the reward search with AutoRL,…
We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw…
A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In …
System uses machine learning and automated reasoning to speed up PBE synthesis.
This dissertation automates deep learning pipelines and uses meta-learning for better model selection and data augmentation.
Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis. However, traditional methods still cannot satisfy practical requirements. In this paper, we explore and adapt various neural network architect…
Framework harmonizes EHR data across institutions for better analysis.
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high ri…
Brain cancer can be very fatal, but chances of survival increase through early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to detect and locate tumors in the brain, and very carefully analyze scans to segment brain tumors. Manual segmentation is time consuming and tiring for doctors, and it ca…
Improves synthetic-to-real generalization without real data.
Researchers develop PAIN to improve self-driving safety through adversarial training.
Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical a…
Automated rock fragmentation assessment using deep learning and spatial statistics.
AutoBayes automates Bayesian graph exploration for robust machine learning.
The paper proposes a method to assess when automated predictions are reliable.
Automated method selects best model from many for production systems.
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a sin…
Work shows hallucination detection by LLMs is impossible without expert feedback.
Over the last twenty five years, advances in the collection and analysis of fMRI data have enabled new insights into the brain basis of human health and disease. Individual behavioral variation can now be visualized at a neural level as patterns of connectivity among brain regions. Functional brain imaging is enhancing…
Proposes a Siamese NN for algorithm selection focusing on alike performing instances.
A new DRL system using LSTM improves stock trading performance.
Quantum effects are known to provide an advantage in particle transfer across networks. In order to achieve this advantage, requirements on both a graph type and a quantum system coherence must be found. Here we show that the process of finding these requirements can be automated by learning from simulated examples. Th…
We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel…