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Found 22 papers, 4 papers with code
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates
Cross-lingual natural language inference (XNLI) is a fundamental task in cross-lingual natural language understanding.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
+3
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs.
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively.
How to Evaluate Semantic Communications for Images with ViTScore Metric?
To evaluate the performance of ViTScore, we compare ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 4 classes of experiments: (i) correlation with BERTScore through evaluation of image caption downstream CV task, (ii) evaluation in classical image communications, (iii) evaluation in image semantic communication systems, and (iv) evaluation in image semantic communication systems with semantic attack.
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleaving
We design a neural network to simulate SOIRE matching and theoretically prove that certain assignments of the set of parameters learnt by the neural network, called faithful encodings, are one-to-one corresponding to SOIREs for a bounded size.
Learning Visual Planning Models from Partially Observed Images
Although a number of approaches have been developed for learning planning models from fully observed unstructured data (e. g., images), in many scenarios raw observations are often incomplete.
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters
In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent.
A DQN-based Approach to Finding Precise Evidences for Fact Verification
Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV.
View-Guided Point Cloud Completion
This paper presents a view-guided solution for the task of point cloud completion.
Ranked #3 on
Point Cloud Completion
on ShapeNet-ViPC
Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification
By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs.
Refining HTN Methods via Task Insertion with Preferences
Hierarchical Task Network (HTN) planning is showing its power in real-world planning.
Representation Learning for Classical Planning from Partially Observed Traces
The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
CoAPI: An Efficient Two-Phase Algorithm Using Core-Guided Over-Approximate Cover for Prime Compilation of Non-Clausal Formulae
The state-of-the-art approaches generate all primes along with a prime cover constructed by prime implicates using dual rail encoding.
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem
Based on the BLP and DTCC, we develop a local search algorithm named BDCC and improve it by a hyperheuristic strategy.
Dependence in Propositional Logic: Formula-Formula Dependence and Formula Forgetting -- Application to Belief Update and Conservative Extension
Dependence is an important concept for many tasks in artificial intelligence.
A General Multi-agent Epistemic Planner Based on Higher-order Belief Change
Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK.
Adversarial Attribute-Image Person Re-identification
While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task.
Query Answering with Inconsistent Existential Rules under Stable Model Semantics
Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule {repair semantics} remain the same as that under the conventional query answering semantics.
