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.
New method uses limited labeled data and multiple starts to adapt models across domains.
Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy. Regrettably, because random features are target-agnostic, typically thousands of such features are necessary to achieve accept…
Tricks adversarial attacks to target specific classes, improving classifier accuracy.
In this paper, we introduce a novel concept for learning of the parameters in a neural network. Our idea is grounded on modeling a learning problem that addresses a trade-off between (i) satisfying local objectives at each node and (ii) achieving desired data propagation through the network under (iii) local propagatio…
Transformers use ReLUs to approximate softmax efficiently.
Detecting and aggregating sentiments toward people, organizations, and events expressed in unstructured social media have become critical text mining operations. Early systems detected sentiments over whole passages, whereas more recently, target-specific sentiments have been of greater interest. In this paper, we pres…
SMART-FAN-Lasso fine-tunes neural networks for high-dimensional nonparametric regression.
AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with "known" number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary…
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon targe…
MACER accelerates error repair by modularly identifying and applying fixes.
Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampl…
Proposes a taxonomy for economic policies.
New model uses pretrained biochemical language models to generate drug compounds.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
Investors target specific regions of payoff distributions for portfolio optimization.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined…
Unified framework LPCD optimizes quantization of complex submodules.
DSVNP uses global and local latent variables for improved neural process predictions.
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capable edge devices with limited computation and storage requires streamlined methodologies that can effi…
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be …
Efficient inference for adaptive data with directional stability condition.
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value. The need for highly confident models with an extremely frugal acquisition of features can be addressed by allowing a feature…
A co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
Bayesian graph learning improves graph representation accuracy.
Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an extensive search is performed in the design space by trying out a large number of design combinations before reaching to the target characteris…
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs. Works on securing neural networks against adversarial examples achieve high empirical robustness on simple datasets such as MNIST. However, these techniques are inadequate when empirically tested on comp…
Cellina uses supervised disentanglement to predict cell behavior in tissues.
The paper uses machine learning to optimize rework policies in semiconductor manufacturing.
This paper automates mining of COVID-19 scholarly articles using machine learning.
Non-intrusive algorithm corrects corrupted data in classification models.
In safety-critical applications of machine learning, it is often important to abstain from making predictions on low confidence examples. Standard abstention methods tend to be focused on optimizing top-k accuracy, but in many applications, accuracy is not the metric of interest. Further, label shift (a shift in class …
Data poisoning attacks can severely degrade FL models, especially targeting specific classes.
We use diffusion models to sample from complex GP priors in climate data.
A new model of learning corrects for chance to improve learning outcomes.
Supervised learning models, also known as quantitative structure-activity regression (QSAR) models, are increasingly used in assisting the process of preclinical, small molecule drug discovery. The models are trained on data consisting of a finite dimensional representation of molecular structures and their correspondi…
Study on testing two populations with confounders.
Mathematical study of instanton corrected q-map spaces and their isometries.
Corrected misstatements about invariant rank in ECS manifold papers.
New method combines machine learning with data assimilation for model error correction.
Tumblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blogs and 53.3 million daily posts. The popularity of Tumblr provides great opportunities for advertisers to promote their products through sponsored posts. However, it is a challenging task to target specific demog…
Electromagnetic stimulation of the human brain is a key tool for the neurophysiological characterization and diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is one procedure that is commonly used clinically. However, personalized TMS requires a pipeline for accurate head model gener…
Localized Multidirectional Correction improves non-refusal target-response behavior in foundation models.
Theoretical analysis shows LLMs can self-correct responses through in-context learning.
New algorithms improve boosting by optimizing chance-corrected measures.
Study examines corrections to heterotic geometry on SU(3) manifolds.
A model corrects Lithuanian grammatical errors.