Robot framework identifies unknown objects from symbolic descriptions.
problem Processing unmodeled objects in unstructured environments.
method Discriminative probabilistic model interpreting symbolic descriptions.
result Improved identification performance through ensemble learning.
Model-based machine learning improves communication systems.
problem Improving symbol detection in communication receivers.
method Review and comparison of model-based and deep learning approaches, focusing on deep unfolding and DNN-aided hybrid algorithms.
result Different strategies of conventional deep architectures and hybrid algorithms show advantages and drawbacks.
CHOOSE enhances shallow Transformers for wireless symbol detection.
problem Improving wireless symbol detection with shallow Transformers.
method Introducing autoregressive latent reasoning steps within hidden space.
result Lightweight Transformers achieve comparable performance to deep models.
Data-driven symbol detection improves performance in complex channels.
problem Designing robust symbol detectors in systems with poorly understood channels.
method Hybrid approach combining model-based algorithms with machine learning.
result Near-optimal performance of model-based algorithms achieved without channel model knowledge.
Study compares symbolic and distributional methods for relational learning.
problem Comparing symbolic and distributional paradigms for relational learning.
method Comparison of representation learning and relational learning on various tasks.
result Preliminary results suggest possible indicators for choosing between approaches.
The paper optimizes learning Gaussian Processes in distributed systems with minimal communication.
problem Learning Gaussian Processes in distributed and communication-limited systems.
method Information-theoretic bounds and practical schemes like per-symbol quantization.
result The proposed methods outperform previous zero-rate distributed GP learning schemes.
Study explains Zipf's law using geometric mechanisms from a finite alphabet.
problem Explains Zipf's law in language without relying on linguistic elements.
method Uses the Full Combinatorial Word Model (FCWM) to generate geometric distributions of word lengths.
result Supports predictions of power-law rank-frequency curves, matching various languages.
DEFINED improves wireless symbol detection with limited pilot data.
problem Efficient symbol detection over block-fading channels with scarce pilot data.
method In-context learning with decision feedback mechanism.
result Significant performance improvements, often needing only a single pilot pair.
AI agents beat previous best on NetHack, but symbolic bots still outperform.
problem Developing AI agents that can ascend in the NetHack game.
method Used a procedurally generated NetHack Learning Environment for reinforcement learning.
result Symbolic bots outperform neural approaches on NetHack.
End-to-end deep learning optimizes optical fiber communication systems.
problem Optimizing the performance of optical fiber communication systems.
method Implementing an end-to-end deep neural network for transmitter, channel, and receiver optimization.
result Achieved bit error rates below 6.7% hard-decision forward error correction threshold.
Paper proposes a new Markov model for efficient PLC system design.
problem Efficient estimation of Markov model parameters for bursty error channels.
method Introduced a Block Diagonal Markov model and a modified Baum-Welch algorithm.
result Efficient estimation of state transition matrix Λ Λ Λ for PLC system design. ViterbiNet uses deep learning to detect symbols without channel info.
problem Symbol detection in digital receivers without channel state info.
method Integrates DNNs into Viterbi algorithm, leaving structure intact.
result Performance of ViterbiNet approaches CSI-based Viterbi, tracks dynamic channels.
Paper explores DNNs in modulation recognition with channel effects and adversarial attacks.
problem Automatic modulation recognition in wireless communications.
method Deep neural networks applied to channel models and adversarial perturbations.
result Impact of channel variations and adversarial attacks on DNN performance.
Paper presents a novel neural network for MIMO symbol detection.
problem Handling a variable number of users in MIMO systems.
method Recurrent and permutation equivariant neural network architecture with iterative decoding.
result The neural detector outperforms existing methods in accuracy and efficiency.
New method uses VAEs for blind channel equalization and decoding.
problem Blind channel equalization and decoding without pilot symbols.
method Variational autoencoders (VAEs) for blind channel equalization and decoding.
result Significant improvement in error rate compared to existing methods.
Deep JSCC maps images directly to channel symbols for wireless transmission.
problem Efficient image transmission in noisy wireless channels.
method Joint source and channel coding using CNNs trained jointly.
result Deep JSCC outperforms traditional JPEG/2000 + channel codes at low SNR.
System solves a significant fraction of Bongard problems using visual features and pragmatic reasoning.
problem Solving Bongard problems with intelligent vision systems.
method Image processing, symbolic visual vocabulary, Bayesian inference, pragmatic reasoning.
result Good agreement between induced concepts and Bongard's solutions.
FedRec learns universal receivers for fading channels without channel statistics.
problem Training neural network-based receivers for diverse fading channels without accurate statistics.
method Federated learning of a MAP detector for downlink fading channels.
result Performance approaches MAP without channel statistics, reduced communication overhead.
Neurosymbolic predictors fail to model uncertainty under independence assumption.
problem Neurosymbolic predictors' reliance on independence assumption limits their ability to model uncertainty.
method Formal analysis of NeSy predictors under independence assumption.
result Assuming independence among symbolic concepts prevents NeSy predictors from representing uncertainty.
The paper investigates how supervised learning and self-play improve sample efficiency in teaching AI to communicate.
problem Improving sample efficiency in training AI to use natural language.
method Investigates the relationship between supervised learning and self-play, introduces supervised self-play (S2P).
result First training agents via supervised learning followed by self-play outperforms self-play followed by supervised learning.
Paper defends against signal modulation detection attacks.
problem Minimizing intruder accuracy while maintaining receiver reliability.
method Adversarial perturbation of channel input symbols.
result Approach makes wireless communication secure with minimal performance loss.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
New model estimates unknown number of users in asynchronous multiuser communication.
problem Estimating unknown number of users in asynchronous multiuser communication.
method Infinite factorial finite state machine model with Bayesian nonparametric approach.
result Effective recovery of data-generating process for various scenarios.
VaSST uses soft symbolic trees for probabilistic symbolic regression.
problem Efficiently recover symbolic expressions from noisy data.
method Variational inference with soft symbolic trees.
result Superior performance in structural recovery and predictive accuracy.
End-to-end deep learning boosts IM/DD fiber communication over dispersive channels.
problem Improving data transmission over dispersive IM/DD channels with memory.
method Bidirectional recurrent neural network (BRNN) for end-to-end deep learning of the communication system.
result End-to-end SBRNN achieves significant bit-error-rate reduction compared to FFNNs.
Unified framework integrates symbolic planning and HRL for robust decision-making.
problem Combining reinforcement learning and symbolic planning for robust decision-making in dynamic environments.
method Integrates symbolic planning with hierarchical reinforcement learning to guide task execution and improve planning.
result Unified framework leads to rapid policy search and robust symbolic plans in complex domains.
Autoencoders improve communication system performance by optimizing constellation geometry and probability.
problem Optimizing constellation geometry and probability for better communication system performance.
method Leveraging autoencoders to learn capacity-achieving symbol distributions and constellations.
result Learned constellations achieve information rates very close to capacity on AWGN channels and outperform existing methods on fading channels.
Paper tackles structure learning of sparse GGMs over multiple access networks.
problem Estimating sparse Gaussian Graphical Model structure from multiple local machines with limited communication.
method Proposes Signs and Uncoded methods for reliable structure learning under power and bandwidth limitations.
result Both methods can recover the structure with high probability for large enough sample size.
New method for constructing frames for vector distributions with specific symbols.
problem Constructing frames for vector distributions with given Jacobi symbols.
method Introducing Jacobi symbols, Optimal Control ideas, explicit construction of canonical frames, algebraic procedure.
result Explicit construction of canonical frames for all distributions with given Jacobi symbols, description of prolongation algebra for most Jacobi symbols.
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…
Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.
problem Slow convergence in neural-symbolic learning due to error propagation issues.
method Introduces grammar model as symbolic prior and back-search algorithm for efficient error propagation.
result Significantly outperforms RL methods in performance, converging speed, and data efficiency.
Geometric symbols help compute heat invariants.
problem Computing heat invariants efficiently.
method Geometric symbol calculus of pseudodifferential operators.
result Efficient computation of heat invariants.
For an arbitrary Riemannian manifold X X X and Hermitian vector bundles E E E and F F F over X X X we define the notion of the normal symbol of a pseudodifferential operator P P P from E E E to F F F . The normal symbol of P P P is a certain smooth function from the cotangent bundle T ∗ X T^*X T ∗ X to the homomorphism bundle H o m ( E , F ) Hom (E,F) H o m ( E , F ) and dep…
New MIMO constellation design for noncoherent communications reduces hardware complexity.
problem Designing efficient MIMO constellations for noncoherent communications over fading channels.
method Geodesic curves of the Grassmann manifold for structured constellation design.
result Achieves comparable error performance to unstructured designs with reduced hardware complexity.
Symbolic dynamics applied to share prices reveals complex, non-Markovian patterns.
problem Analyzing complex systems like share prices using symbolic dynamics.
method Symbolic dynamics applied to time series of share price returns.
result Nontrivial spectrum of Renyi entropies found, indicating non-Markovian behavior.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.
problem Symbolic knowledge in neural models can make models more susceptible to adversarial attacks.
method Investigated deep probabilistic graphical models that incorporate symbolic knowledge and neural nets.
result Symbolic knowledge can propagate the negative effects of adversarial examples, making models more vulnerable.
Enhances cooperative multi-task SemCom for distributed users.
problem Performance degradation in cooperative multi-tasking due to negative information transfer.
method Federated learning (FL) with semantic-aware task clustering.
result Constructive cooperation across distributed users with semantic-aware task clustering.
We introduce mod 3 triple Milnor invariants and triple cubic residue symbols for certain primes of the Eisenstein number field Q ( − 3 ) \mathbb{Q}(\sqrt{-3}) Q ( − 3 ) , following the analogies between knots and primes. Our triple symbol generalizes both the cubic residue symbol and Rédei's triple symbol, and describes the decomposition…
New technique finds globally optimal symbolic equations.
problem Finding globally optimal mathematical expressions.
method Formulated a mixed integer non-linear program (MINLP).
result Guaranteed global optimality in symbolic regression.
A formula for Rademacher symbols in triangle groups is provided.
problem No specific problem stated; focuses on a mathematical formula.
method Presentation of an explicit formula for Rademacher symbols.
result Generalizes Ghys' proof of modular knot linking numbers.
Complex b b b - 6 j 6j 6 j symbols relate to hyperbolic tetrahedron volumes and determinants.
problem Analyzing asymptotics of complex b b b - 6 j 6j 6 j symbols. method Relating asymptotics to hyperbolic tetrahedron volumes and determinants.
result Complex b b b - 6 j 6j 6 j symbols' asymptotics linked to tetrahedron volumes and determinants. The paper classifies symbols of differential operators on vector bundles.
problem Classifying symbols of linear differential operators on vector bundles.
method Associated tuples of linear operators to non-degenerate symbols and used C. Procesi's results to find rational invariants and equivalence criteria.
result Generators for rational invariants and a criterion for symbol equivalence.
NeSS combines neural and symbolic approaches for better compositional generalization.
problem Lack of compositional generalization in deep learning models.
method NeSS uses a neural network to generate traces, executed by a symbolic stack machine with sequence manipulation.
result Achieves 100% generalization performance across multiple domains.
A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.
problem Challenging task of labeling symbols from CAD drawings.
method Pixel-wise point location via Progressive Gaussian Kernels (PGK) and local offset.
result The proposed method achieves good generalization on real-world CAD images.
Defines transverse symbols for foliated manifolds and proves their K-homology class.
problem Transverse index theory for foliated manifolds.
method Using filtrations of tangent bundles, defining transverse symbols, and constructing equivariant KK-classes.
result Transversally Rockland operators yield a K-homology class and there is a Poincare duality result.
Paper proposes a bijective approach for signal/symbol translation using variational auto-encoders.
problem Extracting symbolic information from signals, especially in music, is challenging and non-generic.
method Turned into a density estimation task, using two variational auto-encoders with additive constraint.
result Bijective signal/symbol translation achieved, allowing both signal-to-symbol and symbol-to-signal inference.
The Wodzicki residue and the cut-off integral extend to classical symbol-valued forms. We show that they obey a Stokes' type property and that the extended Wodzicki residue can be interpreted as a complex residue like the ordinary one. In the case of cut-off integrals, Stokes' property (i.e. vanishing on exact forms) o…