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.
Study complexity in financial market using Shannon entropy.
We discuss the systemic risk implied by the interbank exposures reconstructed with the maximum entropy method. The maximum entropy method severely underestimates the risk of interbank contagion by assuming a fully connected network, while in reality the structure of the interbank network is sparsely connected. Here, we…
Predicts trainability of deep neural networks using reconstruction entropy.
EVODiff optimizes DM inference by reducing conditional entropy, improving image generation.
We propose a permutation-invariant loss function designed for the neural networks reconstructing a set of elements without considering the order within its vector representation. Unlike popular approaches for encoding and decoding a set, our work does not rely on a carefully engineered network topology nor by any addit…
Entropy data replaces classical charts for smooth manifolds.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
PACE-GGM uses Gaussian mechanism for private covariance estimation.
Study uses holography to analyze entanglement entropy in deformed CFTs.
This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction meth…
New method recovers curvature from heat diffusion data.
The interdependent nature of the global economy has become stronger with increases in international trade and investment. We propose a new model to reconstruct the international trade network and associated cost network by maximizing entropy based on local information about inward and outward trade. We show that the tr…
A new method for feature fusion in U-Net decoders using difference-based gating.
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
Tree-AMP simplifies inference in complex tree-structured models.
A good representation for arbitrarily complicated data should have the capability of semantic generation, clustering and reconstruction. Previous research has already achieved impressive performance on either one. This paper aims at learning a disentangled representation effective for all of them in an unsupervised way…
We present a novel synthesis of Fisher information and asset pricing theory that yields a practical method for reconstructing the probability density implicit in security prices. The Fisher information approach to these inverse problems transforms the search for a probability density into the solution of a differential…
We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is mo…
New methods optimize sums of bivariate functions on finite domains.
Analyzes how BPE tokenisation affects corpus statistics and model entropy in transformer models.
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures. However, the prolonged acquisition time compared to helical CT increases the likelihood of rigid patient motion. Rigid motion corrupts the geometry alignment assumed during reconst…
Deep learning speeds up protein mapping entropy calculation.
Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets is strongly affected by the in…
Framework for analyzing dynamic topological changes in point clouds using persistent homology and dynamic optimal transport.
A new method for VAEs improves latent space disentanglement without violating probability laws.
A new deep learning framework for efficient IoT data compression and inference.
Develops statistical framework for resolving reward function ambiguity in inverse reinforcement learning.
This work extends entropic optimal transport to non-product reference couplings, focusing on Gaussian cases.
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoencoder and are used in combination with reconstruction losses. The compression objective encourages sparsity and low entropy in the activatio…
In this paper we estimate the propagation of liquidity shocks through interbank markets when the information about the underlying credit network is incomplete. We show that techniques such as Maximum Entropy currently used to reconstruct credit networks severely underestimate the risk of contagion by assuming a trivial…
Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemi…
EPSTE: A geometric token and deep learning approach to estimating transfer entropy in neuroimaging time series
New method improves sparse signal reconstruction using 1RSB-AMP.
Proposes a link between randomness and compression in deep learning.
This paper proposes a new method for efficient data compression using Bayesian neural networks.
Unified framework for estimating reward functions in competitive games.
New method learns cell trajectories and network interactions from single-cell data.
CNN accurately reconstructs lattice topology with strong thermal fluctuations.
Here we present an application of two maxentropic procedures to determine the probability density distribution of compound sums of random variables, using only a finite number of empirically determined fractional moments. The two methods are the Standard method of Maximum Entropy (SME), and the method of Maximum Entrop…
Diffusion models improve image compression at low bit-rates.
We present several new results on the feasibility of inferring the hidden states in strongly-connected trackable weak models. Here, a weak model is a directed graph in which each node is assigned a set of colors which may be emitted when that node is visited. A hypothesis is a node sequence which is consistent with a g…
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior…
A new method for generative modeling of discrete data using geometric latent subspaces.
We show that classical thermodynamics has a formulation in terms of Hamilton-Jacobi theory, analogous to mechanics. Even though the thermodynamic variables come in conjugate pairs such as pressure/volume or temperature/entropy, the phase space is odd-dimensional. For a system with n thermodynamic degrees of freedom it …
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - s…
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
Improved GAN performance using higher-order Wasserstein moments.
An algorithmically hard phase was described in a range of inference problems: even if the signal can be reconstructed with a small error from an information theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficiently small. This hard phase is typically understood as a metastable bran…