arXiv research

This paper presents a ML-based receiver for SDR that outperforms conventional methods.

We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established usin…

GT estimator shows convergence for Markov samples, improving i.i.d. results.

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

Study approximates operator learning for PDEs using Fourier multipliers.

Graph-based approach repairs programs from diagnostic feedback.

A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, with…

Symbolic regression improved by incorporating prior knowledge.

QABBA improves time series storage efficiency while preserving shape information.

This paper proves properties of uniformly hyperbolic sets and constructs Markov partitions.

Algorithm learns stochastic system dynamics from data.

A hybrid Convolutional VAE predicts crypto volatility surfaces, outperforming single-symbol approaches.

Quantum $6j$-symbols linked to tetrahedra angles and volumes.

Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained …

In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K pro…

This paper considers a transmission control problem in network-coded two-way relay channels (NC-TWRC), where the relay buffers random symbol arrivals from two users, and the channels are assumed to be fading. The problem is modeled by a discounted infinite horizon Markov decision process (MDP). The objective is to find…

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applyin…

New insights into image compression trade-offs with private randomness.

Develops a Bayesian framework for symbolic regression of scientific expressions.

ABBA creates a new symbolic time series representation based on Brownian bridge.

This work adapts RDT for mental program construction, showing benefits and costs.

New MIMO constellation design for noncoherent communications reduces hardware complexity.

A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix $A$, a prior probability vector $Π$, and the state dependent output symbol error probability matrix $B$. Several processes are utilized for estimating $A$, $Π$ and $B$ from a given empirically obtained or sim…

We compute the asymptotical growth rate of a large family of $U_q(sl_2)$ $6j$-symbols and we interpret our results in geometric terms by relating them to volumes of hyperbolic truncated tetrahedra. We address a question which is strictly related with S.Gukov's generalized volume conjecture and deals with the case of hy…

Symbolic grounding in causal dynamics achieves near-infinite temporal consistency.

DEFINED uses decision feedback ICL to detect symbols with minimal pilot data.

Directly proves Brioschi formula for Gaussian curvature.

We prove the Turaev-Viro invariants volume conjecture for a "universal" class of cusped hyperbolic 3-manifolds that produces all 3-manifolds with empty or toroidal boundary by Dehn filling. This leads to two-sided bounds on the volume of any hyperbolic 3-manifold with empty or toroidal boundary in terms of the growth r…

EPSTE: A geometric token and deep learning approach to estimating transfer entropy in neuroimaging time series

PAN+SR tackles scalable symbolic regression for large $p$ datasets.

Deep neural networks approximate option prices in high-dimensional Lévy models efficiently.

Sparse code multiple access (SCMA) has been one of non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G wireless communication networks. Conventional SCMA approaches are confronting remarkable challenges in designing low complexity high accur…

Discover equations of motion from distorted video frames.

Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as ne…

This work improves the Euler method for masked diffusion models, providing tighter convergence guarantees.

GADD accelerates uniform-rate discrete diffusion models by 2 orders of magnitude.

VaSST uses soft symbolic trees for probabilistic symbolic regression.

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary source via position modulation (PM). We then present a simple and …

Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a challenging problem for which traditional algorithms are either impractical or suffer from performance limitations. Several recently proposed learning-based approaches achieve promising results on simple channel models (e.g., i.i.d. Gaussian). Howe…

Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the presence of transceiver impairments. The gains can be harvested with standard binary FEC at no cost w.r.t. conventional BICM.

Bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet. While strong bounds on the expected cumulative error are known, there are only limited results on the distribution of this error. We prove tight high-probabil…

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

For an arbitrary Riemannian manifold $X$ and Hermitian vector bundles $E$ and $F$ over $X$ we define the notion of the normal symbol of a pseudodifferential operator $P$ from $E$ to $F$. The normal symbol of $P$ is a certain smooth function from the cotangent bundle $T^*X$ to the homomorphism bundle $Hom (E,F)$ and dep…

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

Discovering quasipotential equations from data using machine learning.

Enhances safety of 3D object detection neural networks.