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.
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.
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.
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.
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%.
Proposes CSG model to separate semantic and variation factors for OOD prediction.
problem Out-of-distribution examples cause conventional models to mix semantic and variation factors, leading to poor performance.
method Causal Semantic Generative model (CSG) based on causal reasoning, using variational Bayes for efficient learning and prediction.
result CSG can identify semantic factor and improve OOD prediction performance.
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.
Unsupervised segmentation learns features without labels, improving accuracy.
problem Discover and localize semantically meaningful categories in images without annotations.
method Separates feature learning from cluster compactification; distills unsupervised features into discrete semantic labels using a contrastive loss function.
result Significant improvement over prior state of the art on semantic segmentation challenges.
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.
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.
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid …
Statsformer validates and adapts LLM-derived semantic priors for improved supervised learning.
problem Unreliable semantic priors from LLMs can degrade supervised learning performance.
method Adapts LLM-derived feature scores into a family of learner-specific prior-injection mechanisms, calibrating their influence using out-of-fold validation.
result Improves prediction performance by adaptively downweighting unreliable LLM priors, ensuring a guardrailed statistical learning system.
In iterative supervised learning algorithms it is common to reach a point in the search where no further induction seems to be possible with the available data. If the search is continued beyond this point, the risk of overfitting increases significantly. Following the recent developments in inductive semantic stochast…
Proposes AMS-SFE to improve zero-shot learning by aligning semantic feature spaces.
problem Domain shift problem in zero-shot learning due to disjoint seen and unseen data.
method Expands semantic features using an autoencoder and aligns them with visual feature manifold.
result Remarkable performance improvement over existing methods.
MISIM improves code similarity systems with neural learning.
problem Improving accuracy of code semantics similarity systems.
method Context-aware semantics structure and neural code similarity scoring algorithm.
result MISIM achieves 8.08% better accuracy than state-of-the-art systems.
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.
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. By the third kind of Bayes' theorem, we can directly convert a Shannon's channel into an optimized semantic channel. When a sample is not big enough, we can use a truth function with paramete…
The paper teaches robots to navigate by learning costs from expert demonstrations.
problem Teaching robots to navigate autonomously using only expert observations.
method Developed a map encoder and cost encoder to infer semantic class probabilities and a cost function from expert observations.
result Robots can learn to follow traffic rules in a simulator using only semantic observations.
Bayesian algorithm improves word representations using semantic taxonomy.
problem Improving word representations in semantic taxonomy.
method Bayesian Hierarchical Words Representation (BHWR) learning algorithm combining Variational Bayes and semantic taxonomy modeling.
result BHWR produces better representations for rare words.
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.
Tag2Vec learns tag representations in hybrid networks with semantic and hierarchical information.
problem Lack of semantic and hierarchical information in tag networks.
method Tag2Vec model that combines nodes and tags into hybrid networks, using parameterized random walks and hyperbolic Skip-gram model.
result Tag2Vec outperforms other models in learning rich semantic tag representations.
Self-supervised learning of visual semantics in image games.
problem Learning visual semantics in referential emergent language games.
method Investigating the impact of feature extractor weights and tasks on visual semantics, using various image augmentations and additional tasks.
result Communication systems can learn visual semantics in a self-supervised manner by playing the right types of games.
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, or…
The paper uses differentiable rendering to generate semantic counterexamples for improving neural network robustness.
problem Neural networks' brittleness to semantic transformations.
method Differentiable rendering for generating realistic images that model semantic changes, combined with adversarial machine learning attacks.
result Semantic counterexamples improve generalization, robustness, and transferability of neural networks.
In semantic parsing for question-answering, it is often too expensive to collect gold parses or even gold answers as supervision signals. We propose to convert model outputs into a set of human-understandable statements which allow non-expert users to act as proofreaders, providing error markings as learning signals to…
Deep learning model separates syntax and semantics for better language generalization.
problem Standard deep learning methods struggle with systematic generalization in natural language.
method Implemented a Syntactic Attention model that separates syntactic and semantic processing.
result The Syntactic Attention model outperforms standard methods on a compositional generalization task.
Sherlock uses deep learning to accurately detect data types from column headers.
problem Detecting accurate semantic types of data columns for data science tasks.
method Sherlock is a multi-input deep neural network trained on a corpus of 686,765 data columns.
result Sherlock achieves a support-weighted F1 score of 0.89, outperforming existing methods.
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its …
Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies i…
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.
Optimizes contrastive learning with individualized temperatures for better performance on imbalanced datasets.
problem The common practice of using a global temperature parameter ignores the varying semantic similarity across different anchor data.
method Proposes a new robust contrastive loss inspired by distributionally robust optimization (DRO) and an efficient stochastic algorithm for automatic temperature individualization.
result Our method automatically learns a suitable temperature for each sample, improving performance on imbalanced datasets.
In this tutorial, I will discuss the details about how Probabilistic Latent Semantic Analysis (PLSA) is formalized and how different learning algorithms are proposed to learn the model.
Adaptive adjustment of semantic feature space improves zero-shot recognition.
problem Domain shift and hubness problems in zero-shot recognition.
method Adaptive adjustment of semantic feature space.
result Remarkable performance improvement compared to existing methods.
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-…
Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a…
Paper tackles zero-shot learning for semantic image interpretation.
problem Extracting structured semantic descriptions from images requires complete training sets, which are often unavailable.
method Uses Logic Tensor Networks to leverage logical constraints and similarities among relationships in the training set.
result Background knowledge can alleviate the incompleteness of training sets, improving zero-shot learning performance.
The paper reviews techniques for detecting errors in semantic segmentation models.
problem Detecting false positives and false negatives in semantic segmentation models.
method Uncertainty quantification techniques applied to semantic segmentation.
result Techniques for detecting false positives and false negatives are proposed and discussed.
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic feature…
A new ZSL algorithm uses shared sparse representations for unseen classes.
problem Classifying images from unseen classes using only semantic information.
method Coupled dictionary learning to represent visual and semantic features in an intermediate space.
result The proposed method outperforms state-of-the-art ZSL algorithms on benchmark datasets.
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.
SECRET combines ML and NLP for better real-world task classification.
problem Limited integration of semantic relationships in supervised ML.
method SECRET fuses semantic information from NLP with feature space of supervised ML.
result Up to 14.0% accuracy and 13.1% F1 score improvements over traditional supervised learning.
GSANet improves semantic segmentation accuracy with selective and global attention.
problem Semantic segmentation accuracy improvement.
method Global and selective attention mechanism with ASPP and sparsemax.
result GSANet achieves state-of-the-art accuracy on ADE20k and Cityscapes datasets.
This survey analyzes deep learning methods for real-time semantic image segmentation.
problem Efficient and accurate semantic image segmentation for real-time applications.
method Comprehensive analysis of deep learning architectures and techniques for fast inference.
result Best-performing architectures and techniques for real-time semantic image segmentation.
Semantic embeddings improve safety-critical classifier performance.
problem Improving interpretability and error detection in safety-critical neural networks.
method Created embeddings from symbolic domain knowledge, used for misprediction interpretation and error detection, introduced semantic distance for confidence measurement.
result Semantic distance achieves near state-of-the-art performance in a traffic sign classifier, faster than other methods.
Advocates Tversky's model for image similarity learning.
problem Learning Tversky similarity measures from image data.
method Computational approach using Tversky's ratio model.
result Performs well compared to existing methods on image datasets.
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.
New model creates code semantics vectors for better understanding.
problem Improving code understanding and embedding quality.
method Siamese recurrent neural network on Python source code.
result Model significantly outperforms bag-of-tokens embeddings.
New approach decouples skill learning and language grounding for autonomous agents.
problem Autonomous acquisition of skills without external instructions and feedback.
method Language-Goal-Behavior (LGB) architecture with semantic representation.
result Decouples skill learning and language grounding, enabling diversity and strategy switching.