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Found 39 papers, 11 papers with code
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
Large Language Models (LLMs) have achieved remarkable success, demonstrating powerful instruction-following capabilities across diverse tasks.
Unsupervised Multi-document Summarization with Holistic Inference
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
Compositional generalization empowers the LLMs to solve problems that are harder than the ones they have seen (i. e., easy-to-hard generalization), which is a critical reasoning capability of human-like intelligence.
Ranked #5 on
Math Word Problem Solving
on MATH
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
This paper introduces FedMLSecurity, a benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
Analyzing Influential Factors in Human Preference Judgments via GPT-4
The impact of hidden factors on the final judgment is also unclear.
Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture.
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost.
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation
Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels.
OASum: Large-Scale Open Domain Aspect-based Summarization
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #5 on
Text Summarization
on Pubmed
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #5 on
Abstractive Text Summarization
on CNN / Daily Mail
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.
Ranked #2 on
Visual Commonsense Tests
on ViComTe-color
Invariant Aggregator for Defending against Federated Backdoor Attacks
Empirical results on three datasets with different modalities and varying numbers of clients further demonstrate that our approach mitigates a broad class of backdoor attacks with a negligible cost on the model utility.
Federated Meta-Learning for Traffic Steering in O-RAN
In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi.
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
The model will also be used as the base model for adaptive training in the new environment.
Multimodal sensor fusion in the latent representation space
In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks.
Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model
Measuring customer experience on mobile data is of utmost importance for global mobile operators.
Sim2real for Reinforcement Learning Driven Next Generation Networks
One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment.
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 for 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.).
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing
However, to realize efficient GNN training is challenging, especially on large graphs.
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.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system.
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.
Large-Scale Person Re-Identification
Video-Based Person Re-Identification
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.
Ranked #1 on
Action Recognition
on VIRAT Ground 2.0
