Unsupervised method parses instructional videos into semantic steps.
problem Parsing instructional videos into semantic steps without supervision.
method Joint visual and language model to identify steps and provide descriptions.
result Method discovers semantically correct instructions for various tasks.
Generative model uses graphs to create natural-sounding code.
problem Creating semantically meaningful source code with syntactic and semantic constraints.
method Graph representation for intermediate state, interleaves grammar-driven expansion with graph augmentation and neural message passing.
result Generative model outperforms baselines in generating natural-sounding code.
New approach uses SPG for semantic communication without a known channel model.
problem Designing efficient semantic communication systems without a known channel model.
method Applying Stochastic Policy Gradient (SPG) for reinforcement learning.
result Achieves comparable performance to model-aware approaches with a decreased convergence rate.
The paper proposes using semantic neighbors to decide when to stop learning.
problem Stopping point in iterative learning algorithms to avoid overfitting.
method Semantic stopping criteria based on inductive semantic stochastic methods.
result The proposed criteria detect stopping points leading to competitive generalization.
IdBench benchmarks semantic representations of identifiers, revealing strengths and weaknesses.
problem Evaluating semantic representations of identifiers in source code.
method Created a benchmark using developer ratings, evaluated natural language and source code embeddings, and compared lexical string distance functions.
result No single technique provides a satisfactory representation of semantic similarities, but ensemble models can improve performance.
Enhances image classification by integrating semantic hierarchy into CNN models.
problem Limited use of external guidance in image classification.
method Integrates label-hierarchy knowledge into CNN-based classifiers and uses order-preserving embeddings.
result Boosts image classification performance through semantic hierarchy integration.
A new method improves topic modeling accuracy using semantic filtering.
problem Improving topic modeling accuracy in text documents.
method Three-step process: generate word/word-pair, apply TF-IDF, merge similar semantic pairs.
result Improves topic accuracy by up to 12.99% compared to state-of-the-art models.
Novel framework for whole-slide blood cell segmentation.
problem Semantic segmentation of whole-slide blood cell images.
method Convolutional encoder-decoder framework with VGG-16 feature extraction.
result Outstanding accuracy (97.18% global accuracy) in classwise segmentation.
ScoreAG generates unrestricted adversarial images maintaining semantic integrity.
problem Limited robustness evaluations due to ℓp-norm constraints. method Score-Based Adversarial Generation (ScoreAG) using score-based generative models.
result ScoreAG improves robustness assessments across multiple benchmarks.
A two-step approach uses knowledge graphs for open-world image recognition.
problem Predicting unknown class labels in real-world visual recognition.
method Learn a knowledge-graph representation and an image representation, then predict relationship triples.
result Predictions for unknown class labels are semantically meaningful.
This work tackles semantic validity in graph generation, proposing a regularization framework for variational autoencoders.
problem Ensuring semantic validity in graph generation, especially for combinatorial structures like molecular graphs and protein interaction networks.
method Proposes a regularization framework for variational autoencoders to enforce semantic constraints in graph generation.
result Significantly higher likelihood of sampling valid graphs compared to existing methods.
The thesis introduces methods to use semantic hierarchy in image classification.
problem Limited work in training image classifiers with non-conventional external guidance.
method Injects label hierarchy knowledge into arbitrary classifiers and uses order-preserving embeddings for image classification.
result Both embedding-based models and CNN-classifiers with hierarchical information outperform a hierarchy-agnostic model.
SHMM models human mobility from GPS and text data, overcoming text sparsity.
problem Modeling human mobility from semantic trace data, especially addressing text sparsity.
method SHMM is a multi-modal spherical hidden Markov model that jointly models location, time, and text embeddings on a unit sphere using vMF distribution.
result SHMM outperforms state-of-the-art models in next location prediction and has lower training cost.
CADD improves generative quality by augmenting discrete diffusion with continuous latent space.
problem Loss of semantic information between denoising steps in discrete diffusion models.
method Introduces a framework that augments discrete state space with a continuous latent space, allowing for graded, informative masked tokens.
result CADD improves generative quality across text generation, image synthesis, and code modeling.
HawkesLLM models text generation with temporal influence, improving semantic alignment under limited memory.
problem Path-dependent uncertainty in agentic text-simulation systems.
method HawkesLLM framework separates temporal influence modeling from text generation, using a multivariate Hawkes process and a language model.
result HawkesLLM improves late-stage semantic alignment under a compact prompt-memory budget.
RCAV quantifies model sensitivity to semantic concepts, improving interpretability methods.
problem Lack of semantic interpretability in image classification models.
method RCAV calculates concept gradients and ascent steps to assess model sensitivity to semantic concepts.
result RCAV yields more accurate and robust interpretations of model behavior.
PATOIS synthesizes code from natural language using learned code idioms.
problem Synthesizing general-purpose source code from natural language specifications is challenging.
method PATOIS uses a neural synthesizer that interleaves high-level and low-level reasoning, incorporating learned code idioms from a corpus.
result Using learned code idioms improves the synthesizer's accuracy on semantic parsing datasets.
NCT simplifies one-step generator adaptation to new controls.
problem Adapting one-step generators to new control conditions.
method Noise Consistency Training (NCT) integrates new controls without retraining.
result NCT achieves state-of-the-art controllable generation in a single pass.
Detects object edges and assigns class labels without pixel-level annotations.
problem Semantic boundary and edge detection with image-level labels.
method Proposes a novel strategy to perform edge detection and class assignment using whole image neural nets and backpropagation.
result High pixel-wise scores indicate semantic boundary locations, suggesting edge labels are not needed during training.
FHBI enhances generalization in Bayesian inference with iterative steps in functional spaces.
problem Improving generalization in Bayesian inference models.
method Iterative two-step procedure with adversarial and functional descent steps in a reproducing kernel Hilbert space.
result FHBI consistently outperforms nine baseline methods on the VTAB-1K benchmark.
Simple model classifies traffic tweets from Twitter using word embeddings.
problem Sparsity and curse of dimensionality in bag-of-words representation for traffic tweets.
method Proposes a word embedding-based framework to classify traffic-related tweets from non-traffic ones.
result State-of-the-art test accuracy of 95.9% achieved with a simple model.
Paper aims to bridge semantic gap between ML and InfoSec by labeling malware datasets with behavioral features.
problem Semantic gap between ML and InfoSec communities hinders ML's impact in InfoSec.
method Surveyed existing malware datasets and features, labeled with behavioral features using threat reports.
result Behavioral labeling alters analysis from intent to executable behavior, bridging semantic gap.
Proposes a graph-based text representation for improved sentiment analysis.
problem Lack of effective methods to encode semantic relations in textual data for sentiment analysis.
method Sentence-level graph-based text representation with deep neural network.
result Significantly outperforms existing sentiment analysis approaches on benchmark datasets.
Graph neural networks can perform approximate reasoning in latent space for mathematical statements.
problem Can neural networks perform steps of approximate reasoning in a fixed dimensional latent space?
method Design and conduct an experiment using graph neural networks to predict rewrite-success of mathematical statements in a latent space.
result Graph neural networks can make non-trivial predictions about rewrite-success of statements in latent space.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
Enhances transfer learning with semantic reasoning for robust predictions.
problem Improving robustness of transfer learning models.
method Integrates semantic representations for better knowledge transfer.
result Demonstrated robustness in bus delay and air quality forecasting.
Proposes a new model for clustering passenger trajectories with graphs.
problem Hierarchical trip structure, inaccurate clustering number, and lack of spatial semantic graphs.
method Tensor Dirichlet Process Multinomial Mixture model with graphs and a tensor version of Collapsed Gibbs Sampling.
result Automatic determination of the number of clusters and better cluster quality.
Quantum machine learns faster by reverse annealing on AQCs.
problem Training RBMs on AQCs is hard due to low qubit connectivity.
method Embedding RBM nodes to virtual qubits, semantic quantum search, reverse annealing schedule.
result Reverse annealing accelerates RBM training and improves reconstruction scores.
SemGANs generate pixel-level accurate semantic images.
problem Generating semantic images with pixel-level accuracy.
method Semantic Generative Adversarial Networks (SemGANs).
result SemGANs outperform standard GANs in semantic image generation tasks.
SAM adds semantic attributes to language models for better interpretation and style variation.
problem Improving text interpretation and style variation in language models.
method SAM includes document attributes, scores them, and embeds them into the model's input space.
result SAM generates interpretable texts and shows superior performance on various datasets.
DGE learns event representations from image sequences without manual annotations.
problem Data hunger and domain adaptation issues in self-supervised learning for temporal segmentation.
method Dynamic Graph Embedding (DGE) learns event representations by iteratively updating a graph and its embedding.
result DGE achieves robust temporal segmentation on benchmark datasets, outperforming state-of-the-art methods.
Graph-RISE learns image embeddings for ultra-fine-grained semantics.
problem Learning image representations for fine-grained semantics.
method Graph-regularized neural graph learning framework.
result Graph-RISE outperforms state-of-the-art on image classification and triplet ranking.
Low-dimensional vectors improve semantic understanding of music and language.
problem Noise in shared semantics due to individual brain biases.
method Jointly model multiple brains to learn low-dimensional vector embeddings.
result These embeddings outperform high-dimensional fMRI data in music and language classification.
LEAPS uses semantic models to improve reinforcement learning in diverse environments.
problem Generalizing and adapting to unseen environments in reinforcement learning.
method Hybrid model-based and model-free approach with a multi-target sub-policy and a Bayesian semantic model.
result LEAPS outperforms baselines in visual navigation tasks using diverse indoor scenes.
Mathematical theory explains neural network semantic development.
problem Understanding how neural networks acquire and organize abstract knowledge.
method Mathematical analysis of deep linear networks.
result Exact solutions reveal principles of semantic development.
New Bayes' theorem optimizes semantic channels for machine learning.
problem Class imbalance and semantic meaning evolution in natural language.
method Convert Shannon's channel to semantic channel using third kind of Bayes' theorem.
result CM algorithm explains natural language evolution and improves predictive models.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.
Proposes a method to generate semantically meaningful adversarial examples.
problem Challenges in creating semantically meaningful adversarial examples.
method Captures semantics via manifold learning, perturbs using Gram-Schmidt process, and imposes adversarial constraints.
result Effectively generates adversarial examples that evade existing defenses.
Proposes a knowledge-guided semantic computing network for better neural network performance.
problem Difficulties in designing, interpreting, and predicting neural network performance.
method Knowledge-guided semantic tree and data-driven neural network modules.
result Improved performance with fewer training samples and lower complexity.
ABNN converts pre-trained DNNs into BNNs for reliable uncertainty quantification.
problem Uncertainty quantification in deep neural networks (DNNs) is challenging and critical for real-world applications.
method Adaptable Bayesian Neural Network (ABNN) that transforms pre-trained DNNs into BNNs with minimal overhead.
result ABNN achieves state-of-the-art performance in image classification and semantic segmentation tasks.
Paper surveys semantic segmentation for automated driving, highlighting challenges and solutions.
problem Semantic segmentation for automated driving.
method Taxonomic survey and empirical evaluation of semantic segmentation algorithms.
result Current semantic segmentation algorithms are not tailored for automated driving needs.
This work provides uncertainty intervals for semantic latent variables in disentangled latent spaces.
problem Challenges in providing meaningful uncertainty quantification for semantic information in disentangled latent spaces.
method Uses quantile regression to output heuristic uncertainty intervals, calibrates these intervals to contain true latent values, and propagates them through the generator.
result Reliably communicates semantically meaningful, principled, and instance-adaptive uncertainty in image super-resolution and image completion.
Semantify-NN verifies neural network robustness against semantic perturbations.
problem Verifying robustness of neural networks against semantic adversarial attacks.
method Inserting semantic perturbation layers (SP-layers) into neural networks to verify robustness.
result Semantify-NN significantly improves robustness verification performance over ℓp-norm-based methods. New system preserves message meaning in wireless networks, improving data rate.
problem Efficiently transmitting message meaning in wireless networks.
method Modeling semantics as hidden random variables, using Information Bottleneck for compression.
result 20 dB SNR improvement for semantic communication.
New diffusion models improve counterfactual image generation with semantic control.
problem Challenges in preserving identity, maintaining quality, and ensuring causal model faithfulness in counterfactual image generation.
method Integrates semantic representations into diffusion models through Pearlian causality, introducing spatial, semantic, and dynamic abduction.
result Demonstrates high-level semantic identity preservation and principled trade-offs between faithful causal control and identity preservation.
Service robots learn new tasks more efficiently with ISI, improving query performance and reducing training time.
problem Incremental learning of semantic concepts in multi-relational embeddings for service robots.
method Incremental Semantic Initialization (ISI) that allows new semantic concepts to be initialized in relation to previously learned embeddings.
result ISI improves immediate query performance by 41.4% and reduces the number of epochs to approach model convergence by 78.2%.
Probabilistic inpainting learns multiple plausible images from missing data.
problem Generating multiple plausible images from missing data in images.
method Building a PixelCNN model that learns a distribution of images conditioned on visible pixels.
result The method produces diverse and realistic inpaintings.
Unsupervised scheme ranks sentences in text documents based on semantic importance.
problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.