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Object referring aims to detect all objects in an image that match a given natural language description. We argue that a robust object referring model should be grounded, meaning its predictions should be both explainable and faithful to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-05 Qing Jiang , Xingyu Chen , Zhaoyang Zeng , Junzhi Yu , Lei Zhang

Standard Transformers have a fixed computational depth, fundamentally limiting their ability to generalize to tasks requiring variable-depth reasoning, such as multi-hop graph traversal or nested logic. We propose a depth-recurrent…

Machine Learning · Computer Science 2026-03-24 Hung-Hsuan Chen

We present the natural arguments for the rationality of a recently proposed simple approach for renormalization which is based solving differential equations. The renormalization group equation is also derived in a natural way and…

High Energy Physics - Phenomenology · Physics 2007-05-23 Ji-Feng Yang

This paper proposes a type-and-effect system called Teqt, which distinguishes terminating terms and total functions from possibly diverging terms and partial functions, for a lambda calculus with general recursion and equality types. The…

Programming Languages · Computer Science 2010-12-23 Aaron Stump , Vilhelm Sjöberg , Stephanie Weirich

We aim here to show that reductionism and emergence play a complementary role in understanding natural processes and in the dynamics of science explanation. In particular, we will show that the renormalization group - one of the most…

General Physics · Physics 2010-01-04 Ignazio Licata

Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if…

Computation and Language · Computer Science 2018-12-27 Xinwei Geng , Longyue Wang , Xing Wang , Bing Qin , Ting Liu , Zhaopeng Tu

We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the closest input instance to a given instance for which the forest…

Machine Learning · Computer Science 2023-03-07 Miguel Á. Carreira-Perpiñán , Suryabhan Singh Hada

Semi-unification is the combination of first-order unification and first-order matching. The undecidability of semi-unification has been proven by Kfoury, Tiuryn, and Urzyczyn in the 1990s by Turing reduction from Turing machine immortality…

Logic in Computer Science · Computer Science 2024-02-14 Andrej Dudenhefner

This paper defines a notion of binding trees that provide a suitable model for second-order type systems with F-bounded quantifiers and equirecursive types. It defines a notion of regular binding trees that correspond in the right way to…

Programming Languages · Computer Science 2015-03-20 Neal Glew

The theorem of factorisation forests shows the existence of nested factorisations -- a la Ramsey -- for finite words. This theorem has important applications in semigroup theory, and beyond. The purpose of this paper is to illustrate the…

Logic in Computer Science · Computer Science 2007-05-23 Thomas Colcombet

A class of random recursive sequences (Y_n) with slowly varying variances as arising for parameters of random trees or recursive algorithms leads after normalizations to degenerate limit equations of the form X\stackrel{L}{=}X. For…

Probability · Mathematics 2016-09-07 Ralph Neininger , Ludger Ruschendorf

In this work we study randomised reduction strategies,a notion already known in the context of abstract reduction systems, for the $\lambda$-calculus. We develop a simple framework that allows us to prove a randomised strategy to be…

Logic in Computer Science · Computer Science 2019-11-12 Ugo Dal Lago , Gabriele Vanoni

We present a new method for determining the renormalization of Green functions to all orders in perturbation theory, which we call the displacement operator formalism, or the D-formalism, in short. This formalism exploits the fact that the…

High Energy Physics - Phenomenology · Physics 2009-11-11 Daniele Binosi , Joannis Papavassiliou , Apostolos Pilaftsis

In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods…

Computer Vision and Pattern Recognition · Computer Science 2021-03-10 Mingjie Sun , Jimin Xiao , Eng Gee Lim

Stochastic approximation algorithms are iterative procedures which are used to approximate a target value in an environment where the target is unknown and direct observations are corrupted by noise. These algorithms are useful, for…

Logic in Computer Science · Computer Science 2022-08-10 Koundinya Vajjha , Barry Trager , Avraham Shinnar , Vasily Pestun

Revision programming is a formalism to describe and enforce updates of belief sets and databases. That formalism was extended by Fitting who assigned annotations to revision atoms. Annotations provide a way to quantify the confidence…

Artificial Intelligence · Computer Science 2007-05-23 Victor Marek , Inna Pivkina , Miroslaw Truszczynski

The Ackermann function is a famous total recursive binary function on the natural numbers. It is the archetypal example of such a function that is not primitive recursive, in the sense of classical recursion theory. However, and in seeming…

Logic in Computer Science · Computer Science 2016-02-17 Baltasar Trancón y Widemann

We investigate the structure of Polchinski's formulation of the flow equations for the continuum Wilson effective action. Reinterpretations in terms of I.R. cutoff greens functions are given. A promising non-perturbative approximation…

High Energy Physics - Phenomenology · Physics 2009-10-22 Tim R. Morris

The need for recognition/approximation of functions in terms of elementary functions/operations emerges in many areas of experimental mathematics, numerical analysis, computer algebra systems, model building, machine learning, approximation…

Discrete Mathematics · Computer Science 2021-06-09 Andrzej Odrzywolek

We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally…

Machine Learning · Statistics 2026-01-16 Binh Duc Vu , Jan Kapar , Marvin Wright , David S. Watson
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