Study helicoidal surfaces with singular points using frontals.
problem Investigate helicoidal surfaces with singular points.
method Use frontals in the Euclidean plane to analyze helicoidal surfaces.
result Provide criteria for the singularities of helicoidal surfaces of frontals.
Dynamic steerable blocks improve deep networks by learning filter invariances.
problem Pixel-based filters ignore image properties, leading to suboptimal performance.
method Developed frame-based ResNets and Densenets, which are steerable under predefined transformations.
result Dynamic steerable blocks outperform other approaches on contour detection datasets.
Introduces hyperbolic generalized framed surfaces and their properties.
problem None explicitly stated; focuses on introducing new geometric objects.
method Generalization of hyperbolic framed surfaces and curves.
result Established conditions for a surface to be a hyperbolic generalized framed base surface and explored their singularities.
Paper improves music transcription models with invariance and data augmentation.
problem Improving accuracy of frame-based music transcription models.
method Translation-invariant network combining filterbank and CNN, trained with pitch-shift augmented data.
result Top-performing model in MIREX evaluation, reducing model complexity and avoiding overfitting.
New approach to symmetries in teleparallel geometries with non-trivial isotropy groups.
problem Determining symmetries with non-trivial isotropy groups in teleparallel geometries.
method Introducing a frame-based approach to find the most general Riemann-Cartan geometries that admit a given symmetry group.
result Determine the most general geometries with minimal arbitrary functions for specific symmetry groups.
Study surfaces of revolution from frontals in Euclidean space.
problem Characterize surfaces of revolution from frontals.
method Analyze curvatures and invariants of Legendre curves to derive properties of surfaces of revolution.
result Properties of surfaces of revolution with singularities and cones are defined.
We consider the problem of dynamic buying and selling of shares from a collection of N stocks with random price fluctuations. To limit investment risk, we place an upper bound on the total number of shares kept at any time. Assuming that prices evolve according to an ergodic process with a mild decaying memory proper…
Study on singular points of translation surfaces under linearly dependent conditions.
problem Investigate singular points of translation surfaces under linearly dependent conditions.
method Use theories of generalised framed surfaces and framed surfaces.
result Introduce translation generalised framed surfaces and investigate their singular points.
Hotel booking chatbot handles daily tens of thousands of searches.
problem Improving hotel booking through conversational AI.
method Frame-based dialogue management system with ML models for intent, entity recognition, and info retrieval.
result Chatbot deployed on a commercial scale, handling hotel searches daily.
A treatment in a neighborhood and at a point of the equivalence principle on the basis of derivations of the tensor algebra over a manifold is given. Necessary and sufficient conditions are given for the existence of local bases, called normal frames, in which the components of derivations vanish in a neighborhood or a…
A new batch optimisation framework using Natural Gradient improves DNN acoustic models.
problem Optimizing DNN acoustic models for better word error rate approximation.
method Proposes a Natural Gradient (NG) approach to sequence training, correcting the gradient based on local curvature of KL-divergence.
result The NG method converges more quickly and can be applied to any sequence discriminative training criterion.
The paper studies helicoidal surfaces of non-lightlike frontals in Lorentz-Minkowski 3-space.
problem Investigating the properties and singularities of helicoidal surfaces in Lorentz-Minkowski space.
method Defining and analyzing two types of helicoidal surfaces, using diffeomorphic transformations and criteria for cusps and cuspidal edges.
result Identification theorems for the singular types of both 1-type and 2-type helicoidal surfaces.
We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. As opposed to unsupervised initialisation followed by supervised fine tuning, our method takes advantage of both unlabelled and labelled data…
TANNs integrate thermodynamics into ANN models for accurate, consistent predictions.
problem Lack of rigorous physics-based approach in ANN constitutive modeling.
method TANNs encode thermodynamics principles in neural network architecture using automatic differentiation.
result TANNs produce thermodynamically consistent predictions without requiring large datasets.
Classical approaches to isometric embedding simplified.
problem Isometric embedding of Riemannian surfaces in Euclidean 3-space.
method Coordinate-based and moving-frames approaches, focusing on integrability of PDEs.
result The integrability of the PDE is surprisingly easy and related to moving frames approach.
Rolling Diffusion improves video prediction by progressively corrupting frames based on their temporal position.
problem Improving video prediction accuracy by accounting for temporal dynamics.
method A sliding window denoising process that assigns more noise to frames that appear later in a sequence.
result Rolling Diffusion outperforms standard diffusion models in tasks with complex temporal dynamics.
Paper tackles fall detection using adversarial learning.
problem Detecting falls in the absence of training data due to class imbalance.
method Adversarial learning framework with spatio-temporal autoencoder and convolution network.
result Proposed framework outperformed baseline methods on publicly available datasets.
End-to-end FCN framework optimizes speech enhancement metrics.
problem Inconsistency between model optimization and evaluation metrics.
method End-to-end utterance-based FCN for direct optimization of STOI.
result Enhanced speech has better STOI and improved intelligibility.
Deep RL learns to play Pong from frames alone.
problem Scaling up RL problems leads to computational bottlenecks.
method End-to-end DRL approach using ANN and Policy Gradients.
result Successfully learned to play Pong from frames.
IRL addresses weaknesses in DDPG and A3C for continuous reinforcement learning.
problem Theoretical weaknesses in DDPG and A3C for continuous reinforcement learning.
method IRL based on stochastic differential equations, ensuring action continuity and variance control.
result IRL method guarantees action continuity and variance control, allowing positive interaction with the environment.
Paper presents a new video compression method using autoencoders.
problem Efficient video compression with reduced quality loss.
method 3D autoencoder with discrete latent space and autoregressive prior trained jointly.
result Method outperforms state-of-the-art learned video compression networks.
Real-time drowsiness detection on mobile phones reduces road trauma.
problem Driver drowsiness increases crash risk and road trauma.
method Depthwise separable 3D convolutions combined with early fusion of spatial and temporal information.
result Real-time drowsiness detection on mobile phones reduces road trauma.
MaskCycleGAN-VC improves voice conversion without parallel data.
problem Limited ability to convert mel-spectrogram data without parallel data.
method Integrates a novel auxiliary task called filling in frames (FIF) to learn time-frequency structures.
result MaskCycleGAN-VC outperforms existing methods with similar model size.
Introduces K-framings for surfaces, generalizing quadratic forms.
problem Generalizing quadratic forms to commutative rings with unit.
method Introduces K-framings and maps based loops to homology classes. result Bijection between K-framings and twisted cocycles for surfaces with positive genus. End-to-end training improves segmental models for speech recognition.
problem Training segmental models is challenging and lacks understanding under different settings and losses.
method Investigated a model class based on recent successful approaches, trained end-to-end with various losses.
result Marginal log loss provides the most consistent strong performance without requiring ground-truth alignments.
New kinematic model for a spin-rolling sphere using Darboux frame.
problem Control and planning of an underactuated sphere on a plane.
method Darboux frame transformation to fully-actuated model.
result Underactuated sphere model transformed into fully-actuated model.
New framework aligns latent representations over-the-air using intelligent metasurfaces.
problem Heterogeneous transmitter-receiver models produce misaligned latent representations in semantic communication.
method Intelligent metasurfaces (SIM) emulate supervised and zero-shot semantic aligners directly in the wave domain.
result SIMs achieve up to 90% task accuracy in high SNR regimes, robust to low SNR.
Improved modeling of chaotic systems using time-delay embeddings and Frenet-Serret frame.
problem Identifying effective coordinate systems for nonlinear dynamical systems.
method Developed a new algorithm to identify more stable and accurate models from less data, leveraging the connection between HAVOK and Frenet-Serret frame.
result The sub- and super-diagonal entries of the linear model correspond to intrinsic curvatures in Frenet-Serret frame.
This paper morphs images on manifold-valued spaces using discrete geodesics.
problem Morphing manifold-valued images with discrete geodesics.
method Time discrete geodesic paths model, numerical minimization alternating between deformations and images.
result Existence of minimizing sequences for morphing manifold-valued images.
New RLHF algorithm identifies optimal policies from human feedback without explicit reward inference.
problem Training large language models with human feedback without reward inference.
method Model-free RLHF algorithm BSAD that identifies optimal policies directly from human preference. result Provable, instance-dependent sample complexity ildeO(cMSA3H3Mlogδ1). Survey on deep learning for social network analysis.
problem Encoding social network data into useful low-dimensional representations.
method Review of neural network models for node and subgraph embeddings in various network types.
result Advancements in deep learning for complex network analysis.
RFN improves GCNs for road networks, outperforming state-of-the-art by 21%-40%.
problem Leveraging the structure of road networks effectively in machine learning tasks.
method Introducing RFN, a novel GCN specifically designed for road networks.
result RFN outperforms state-of-the-art GCNs by 21%-40% on road network tasks.
New model reconstructs networks by identifying regular components.
problem Uncovering the complexity of network structures.
method Low-rank pursuit based self-representation network model.
result Reconstructs networks and measures their regulability.
Algorithm reconstructs conserved networks from flow data.
problem Network reconstruction from flow data.
method Polynomial time algorithm exploiting graph theoretic properties and learning techniques.
result Exact network reconstruction possible for arborescence networks.
This survey clarifies dynamic network terminology and reviews GNN models for dynamic networks.
problem Ambiguity in dynamic network terminology and lack of GNN models for dynamic networks.
method Established consistent terminology and notation for dynamic networks, reviewed GNN models.
result Comprehensive survey of dynamic graph neural network models.
DCNs mimic neuronal networks for improved neural classification.
problem Lack of topological similarity between DNNs and biological neural networks.
method Developed DCNs with topologies inspired by real-world neuronal networks.
result High classification accuracy achieved by DCNs.
Study 986 diverse networks to reveal structural diversity across domains.
problem Understanding structural diversity in networks across various domains.
method Machine learning techniques (random forest, confusion matrix) on 986 real-world networks and 575 generated networks.
result Networks in the same partition have similar underlying functions, constraints, and generative mechanisms, regardless of their origins.
Network recasting transforms network architecture for faster inference.
problem Accelerate inference process through network transformation.
method Block-wise recasting of source blocks in a teacher network to target blocks in a student network.
result Transforms network architecture while preserving accuracy and reducing inference time.
Highly accurate classification of network categories achieved.
problem Distinguishing between different types of networks (e.g., social vs. web graphs).
method Used a random forest classifier on both real-world and synthetic networks.
result Achieved a 94.2% classification accuracy.
Network Lens identifies node behaviors in heterogeneous networks with high accuracy.
problem Identifying different behaviors in various parts of large heterogeneous networks.
method Zoom into network using different-sized lenses to capture local structure, weight signatures to predict node labels.
result Achieved a peak accuracy of ~42% on two networks with ~100,000 and ~1,000,000 nodes, significantly better than random.
DANE adapts network embeddings across multiple domains.
problem Learning embeddings for multiple networks without transferability.
method Graph Convolutional Network with adversarial learning.
result DANE achieves superior performance in cross-network domain adaptation.
Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
problem Vulnerability of capsule networks to adversarial attacks.
method Compared capsule networks to convolutional neural networks using various adversarial attacks.
result Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
Chemical networks outperform spiking neural networks in classification tasks.
problem Learning tasks with spiking neural networks require hidden layers, which are computationally expensive.
method Used deterministic mass-action kinetics to prove chemical reaction networks without hidden layers can solve tasks previously solved by spiking neural networks.
result A chemical reaction network without hidden layers outperforms a spiking neural network with hidden layers in a handwritten digit classification task.
Deep ReLU networks can be simplified to a three-layer model.
problem Understanding the behavior of deep neural networks.
method Constructive proof and algorithm to transform deep networks into shallow ones.
result Deep ReLU networks can be represented by a simpler three-layer structure.
Tackles network structure inference from time series data using GNN.
problem Inferring network structure from incomplete or no information.
method Gumbel Graph Network (GGN) model for network reconstruction and completion.
result GGN can reconstruct up to 100% network structure and infer missing parts with up to 90% accuracy.
Paper proposes algorithms for embedding directed networks with text associated nodes.
problem Learning embeddings for directed networks with text associated nodes.
method PCTADW-1 and PCTADW-2 neural network algorithms.
result Embeddings improve node classification quality on software package dependency networks.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
New method identifies common organizational principles in networks.
problem Challenging to cluster networks of different size and density.
method Introduces a new network comparison methodology.
result Identifies common organizational principles in networks.