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.
This paper explores what causal structures can be distinguished by observational and interventional probing schemes.
We study the statistical behavior of reasoning probes in a stylized model of iterative computation inspired by neural algorithmic reasoning. The underlying computation is given by a looped Boolean circuit whose graph is a perfect -ary tree (), with outputs recursively fed back as inputs across computation ro…
Entropy data replaces classical charts for smooth manifolds.
This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests o…
Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood. Data-driven analysis of probe performance can be used to quantify the uncertainty in individual probes and to highlight the relative contribution of different noise…
A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both…
New method tightens federated probe-logit distillation rates under varying bandwidths.
We identify spectral conditions for reliable neural probe interpretation.
Optimal probing framework for scalable network monitoring.
Paper introduces Manifold Probe for discovering representation manifolds in superposition.
OLPA optimizes online user-centric selection with probing, achieving near-optimal regret bounds.
BoC probe assesses neural network confidence coherence, revealing architecture-specific uncertainty.
This paper studies two important signal processing aspects of equilibrium behavior in non-cooperative games arising in social networks, namely, reinforcement learning and detection of equilibrium play. The first part of the paper presents a reinforcement learning (adaptive filtering) algorithm that facilitates learning…
Paper analyzes why deeper layers of ViTs perform worse on out-of-distribution tasks.
System guides freehand obstetric ultrasound probe movements.
The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an a…
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural …
Study examines flaws in probing LLMs' knowledge and introduces a new method.
This paper addresses missing covariates in stochastic linear bandits, providing a high-probability regret bound.
Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar photovoltaics and energy storage units are interfaced via invert…
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an effic…
New method infers unknown parameters in quantum sensing with high probability.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
This research discovers model architecture and training dataset characteristics through strategic input probing.
Researchers create holographic super-embeddings for M5 and M2 branes.
Noise Injection probes deep learning dynamics during training phases.
We probe the character of knotting in open, confined polymers, assigning knot types to open curves by identifying their projections as virtual knots. In this sense, virtual knots are transitional, lying in between classical knot types, which are useful to classify the ambiguous nature of knotting in open curves. Modell…
We study supersymmetric probe M5-branes in the AdS_4 solution that arises from M5-branes wrapped on a hyperbolic 3-manifold M_3. This amounts to introducing internal defects within the framework of the 3d-3d correspondence. The BPS condition for a probe M5-brane extending along all of AdS_4 requires it to wrap a surfac…
Accumulation of standardized data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of contemporary microarray collections. While short oligonucleotide arrays …
PROBE algorithm efficiently solves sparse high-dimensional linear regression.
This work defines idealized SSL representations and improves existing methods.
Telescope detects LLM generated text by measuring token repetition probability.
Higher gauge theory via differential nonabelian cohomology
Concept Hierarchies and Formal Concept Analysis are theoretically well grounded and largely experimented methods. They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets. Galois lattices are visually seducing and conceptually rich for experts. However they present important…
This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an introduction to generative models and provide some personal interpretations supported by empirical evidence. Both generative adversarial netwo…
Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels o…
This paper enhances language models with knowledge awareness.
New method predicts aphasia severity with narrower uncertainty intervals.
Calibrating classifiers reduces grouping loss using sufficiency criteria.
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The W…
Detects anomalies in multiple processes using hidden Markov models.
Neural network models have a reputation for being black boxes. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. This helps us better understand …
Researchers propose a new SSL risk decomposition method to evaluate and improve self-supervised learning models.
The paper evaluates the probability distributions of analog-to-target distances for multiple analogs.
Bayesian imaging methods deliver trustworthy probabilities in some cases but struggle with uncertainty quantification.
This work examines the problem of graph learning over a diffusion network when data can be collected from a limited portion of the network (partial observability). The main question is to establish technical guarantees of consistent recovery of the subgraph of probed network nodes, i) despite the presence of unobserved…
We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probe…