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.
SketchGraphs dataset aids in modeling CAD designs.
This paper reviews deep learning and knowledge-based methods for molecular design.
Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the alternating direction method of multipliers (ADMM)-penalized decoder. In addition, we pr…
High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametr…
AIDA designs personalized audio processing algorithms for hearing aids.
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly c…
This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives an…
New method for calculating crosscap numbers of knots.
The paper develops a causal machine learning framework to optimize aid allocation.
Analyzes surfaces minimizing mean curvature variation using PDEs.
We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…
The paper analyzes convergence rates of bilevel optimization algorithms and introduces a new stochastic algorithm.
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input at…
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and varied, but inevitably amongst the reasons given is that the systems designed often do not perform as expected by their designers. The reasons…
Bayesian emulator tackles models with discontinuities efficiently.
Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagn…
Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, nam…
A deep learning model predicts future cache accesses with high accuracy.
Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20% of hearing aid patients. This paper proposes a probabilistic modeling approach to the de…
Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both …
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
TinyLSTMs reduces speech enhancement model size and latency for hearing aids.
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
Dataset of 4500 bicycle designs aids in design analysis and synthesis.
Paper proposes AggITD for efficient federated hypergradient computation.
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of th…
Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning. However currently in …
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning t…
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
With the aid of a computer, we provide a motion picture of the twist-spun trefoil which exhibits the periodicity well.
Method for creating synthetic multi-fidelity data sets.
Survey on automating geometry problem solving with large models.
A new method studies symplectic configurations in rational 4-manifolds using computer-aided techniques.
Authors determine the SL(2,C) character variety of a specific knot without computer aid.
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
In this paper, a neural network-aided bit-interleaved coded modulation (NN-BICM) receiver is designed to mitigate the nonlinear clipping distortion in the LDPC coded direct currentbiased optical orthogonal frequency division multiplexing (DCOOFDM) systems. Taking the cross-entropy as loss function, a feed forward netwo…
I-AID categorizes disaster tweets into useful information types.
Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of dimensionality problem, and the generalization issues. One of the main difficulties is that there exists computational cost problem in dealing with input data of large size matrices which repre…
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power. Consequently, there is a growing need for the development of data-driven and, in particular, deep learning methods for various tasks such …
Bayesian optimization improves with transfer learning for aircraft design.
AI-Interpret transforms opaque policies into simple, interpretable decision rules.
Machine learning speeds up RIS design for efficient RF components.
New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.
Homological stability aids in computing group homology.
RAD estimates gradients with less memory, faster than small batch sizes.
This paper analyzes the impact of loops on bilevel optimization efficiency.