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Found 18 papers, 4 papers with code
Towards Abstractive Grounded Summarization of Podcast Transcripts
Summarization of podcast transcripts is of practical benefit to both content providers and consumers.
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
To this end, we propose a strategy to mitigate the effect of spurious features based on our observation that the global model in the federated learning step has a low accuracy disparity due to statistical heterogeneity.
AppQ: Warm-starting App Recommendation Based on View Graphs
In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
Self-play Learning Strategies for Resource Assignment in Open-RAN Networks
Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC.
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
To facilitate the research on this corpus, we provide results of several benchmark models.
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning
However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks.
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision
Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process.
Automatic Summarization of Open-Domain Podcast Episodes
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions.
Lipschitz Learning for Signal Recovery
We will develop a theoretical framework to characterize the signals that can be robustly recovered from their observations by an ML algorithm, and establish a Lipschitz condition on signals and observations that is both necessary and sufficient for the existence of a robust recovery.
Location Anomalies Detection for Connected and Autonomous Vehicles
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system.
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix.
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary.
Video Event Recognition With Deep Hierarchical Context Model
Video event recognition still faces great challenges due to large intra-class variation and low image resolution, in particular for surveillance videos.
A Hierarchical Context Model for Event Recognition in Surveillance Video
These three levels of context provide crucial bottom-up, middle level, and top down information that can benefit the recognition task itself.
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