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.
Article evaluates AI security threats and proposes multiple measures.
We report an extension of a Keras Model, called CTCModel, to perform the Connectionist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a co…
We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer-by-layer a compositional hypothesis class, i.e., a feedforward, multilayer architecture, in a supervised setting. In terms of the models, we present a principled method to "kernelize" (partly or complet…
In this paper we demonstrate end-to-end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition…
In this paper we investigate whether electroencephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems. We implemented a connectionist temporal classification (CTC) based end-to-end automatic speech recognition (ASR) model for performing recognition. Our result…
This dissertation explores the integration of learning and analogy-making through the development of a computer program, called Analogator, that learns to make analogies by example. By "seeing" many different analogy problems, along with possible solutions, Analogator gradually develops an ability to make new analogies…
In this paper we explore continuous silent speech recognition using electroencephalography (EEG) signals. We implemented a connectionist temporal classification (CTC) automatic speech recognition (ASR) model to translate EEG signals recorded in parallel while subjects were reading English sentences in their mind withou…
Enhances neural networks with logical knowledge for better performance.
Learning image transformations is essential to the idea of mental simulation as a method of cognitive inference. We take a connectionist modeling approach, using planar neural networks to learn fundamental imagery transformations, like translation, rotation, and scaling, from perceptual experiences in the form of image…
In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction of such models. In this work, we argue that dynamically bound f…
This paper explores BDL hyperparameters for robust polynomial mapping with noise.
In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce tech…
One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its back-end. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learni…
In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limit…
AI stocks hedge against AI singularity's economic impact.
Study analyzes AI's impact on firms, markets, and workers using large language model data.
New AI stock indices classify firms' AI engagement using 10-K filings.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this lim…
This review covers AI in finance, challenges, techniques, and opportunities.
Explainable AI improves human decision accuracy but does not enhance it significantly.
The paper explores AI in finance, focusing on XAI's role in enhancing interpretability and trust.
Paper defines AI-specific loss reconstruction problem and introduces CER framework.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
AI enhances financial forecasting with challenges in regulation and privacy.
AI+MPS workshop aims to strengthen AI's role in science.
Improved AI patent classifier measures U.S. and China's AI patenting.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic m…
Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable performance in the area of speech and handwriting recognition. The performance of an MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by using Hessian-free (HF) optimization. Given…
AI threatens financial stability through misuse and stealth adoption.
Adversarial policies beat superhuman Go AI systems.
The DoD needs a robust process to evaluate AI/ML model performance and robustness.
Paper tackles AI risks by customizing metrics and models.
Qlib aims to integrate AI into quantitative investment.
Open AI models affect bond yields differently than closed ones.
AI systems are being deployed to support human decision making in high-stakes domains. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI's inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including …
Do-AIQ framework evaluates AI algorithms' quality using DOE.
AI enhances ESG practices in finance, but requires careful consideration.
Generative AI boosts analyst reports but increases forecast errors.
Improves AI-prior reliability for Bayesian inference.
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
The paper discusses safety assessment for AI systems, focusing on machine learning models.
ChatGPT launch boosted AI-related crypto assets by 10.7% to 15.6%.
Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one…
Generative AI boosts productivity and improves customer service quality.
AI attacks threaten insurance systems, requiring new defenses.
AI aids in mathematics research and problem-solving.