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 framework detects near vs. far out-of-distribution samples for AI safety.
LLMs show surprising confidence in their answers, beyond just tokens.
Auto-Surprise automates recommender system selection and optimization.
iREPA shows spatial structure, not global semantic, drives generation performance in REPA.
Unifies 18 definitions of surprise, classifies them into four categories.
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semant…
Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find th…
Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The mod…
DG separates successes and failures by gating updates with advantage and surprisal.
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scenarios. The case study investigated is motivated by a setup where an aerial robot acts as a "scout" for a ground robot. This is useful when t…
Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We introduce a new definition of surprise and its RL implementation named Variation…
EMIX minimizes surprise in multi-agent reinforcement learning.
MIME uses mutual information minimization for better exploration in environments with abrupt transitions.
Proves that emergent algebras right-distributivity implies left-distributivity.
The Surprise index assesses autonomous systems' competency in uncertain environments.
Derives time-averaged active inference from control principles.
DE is a new exploration method that limits resource usage based on expected improvement and surprise.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
Paper uses surprisal to dynamically allocate computation between fast and slow models.
Curiosity-driven exploration using Bayesian surprise in latent space.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
Model compresses event-like contexts using gated surprise signals.
In this paper, we describe a new surprising example of a fibration of the Clifford torus S3 x S3 in the 7-sphere by great 3-spheres, which is fiberwise homogeneous but whose fibers are not parallel to one another. In particular it is not part of a Hopf fibration. A fibration is fiberwise homogeneous when for any two fi…
The purpose of the present paper is to introduce and explore two surprises that arise when we apply a standard procedure to study the number of finite type invariants of 3-manifolds introduced independently by M. Goussarov and K. Habiro based on surgery on claspers, Y-graphs or clovers, \cite{Gu,Ha,GGP}. One surprise i…
Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements an…
Surprising circles found in Coxeter group boundaries.
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement …
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
This work provides uncertainty intervals for semantic latent variables in disentangled latent spaces.
Identifier names convey useful information about the intended semantics of code. Name-based program analyses use this information, e.g., to detect bugs, to predict types, and to improve the readability of code. At the core of name-based analyses are semantic representations of identifiers, e.g., in the form of learned …
New system preserves message meaning in wireless networks, improving data rate.
New diffusion models improve counterfactual image generation with semantic control.
New approach uses SPG for semantic communication without a known channel model.
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Se…
Unsupervised scheme ranks sentences in text documents based on semantic importance.
A model simulates how different types of traders react to macroeconomic news.
SPAT improves adversarial robustness by preserving semantics in adversarial training.
Proposes CSG model to separate semantic and variation factors for OOD prediction.
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid …
Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological s…
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its …
Generative Adversarial Networks (GANs) have obtained extraordinary success in the generation of realistic images, a domain where a lower pixel-level accuracy is acceptable. We study the problem, not yet tackled in the literature, of generating semantic images starting from a prior distribution. Intuitively this problem…
Unsupervised segmentation learns features without labels, improving accuracy.
Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a…
In iterative supervised learning algorithms it is common to reach a point in the search where no further induction seems to be possible with the available data. If the search is continued beyond this point, the risk of overfitting increases significantly. Following the recent developments in inductive semantic stochast…