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Found 15 papers, 9 papers with code
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training
Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI.
Perturbation-Based Two-Stage Multi-Domain Active Learning
Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort.
Multi-Domain Learning From Insufficient Annotations
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains.
Speech4Mesh: Speech-Assisted Monocular 3D Facial Reconstruction for Speech-Driven 3D Facial Animation
Besides, we also verify that the proposed framework is able to explicitly control the emotion of the animated talking face.
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk
We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that are important in risk prediction for particular cancer types, is reliable.
YACLC: A Chinese Learner Corpus with Multidimensional Annotation
This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.
Multi-Domain Active Learning: Literature Review and Comparative Study
Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
Robust Dynamic Network Embedding via Ensembles
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
GloDyNE: Global Topology Preserving Dynamic Network Embedding
The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.
Ownership at Large -- Open Problems and Challenges in Ownership Management
Software-intensive organizations rely on large numbers of software assets of different types, e. g., source-code files, tables in the data warehouse, and software configurations.
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild
And we validate our method on the Aff-Wild2 datasets released by the Challenge.
Facial Expression Recognition
Facial Expression Recognition (FER)
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.
HEAT: Hyperbolic Embedding of Attributed Networks
As a general embedding method, HEAT opens the door to hyperbolic manifold learning on a wide range of attributed and unattributed networks.
Social and Information Networks
Attributed Network Embedding for Incomplete Attributed Networks
Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e. g. a social network with user profiles.
