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Found 18 papers, 5 papers with code
Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging
Efficient compression of remote sensing imagery is a critical solution to alleviate these burdens on satellites.
RedCode: Risky Code Execution and Generation Benchmark for Code Agents
To provide comprehensive and practical evaluations on the safety of code agents, we propose RedCode, a benchmark for risky code execution and generation: (1) RedCode-Exec provides challenging prompts that could lead to risky code execution, aiming to evaluate code agents' ability to recognize and handle unsafe code.
Role-playing Prompt Framework: Generation and Evaluation
Large language models (LLMs) exhibit impressive proficiency in natural language generation, understanding user instructions, and emulating human-like language use, which has led to significant interest in their application to role-playing scenarios.
Ads Recommendation in a Collapsed and Entangled World
We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations.
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets
In order to comprehensively investigate the influence caused by the misalignment, we proposed a method for estimating the performance of a 4f-ONN in response to various misalignment in the context of the image classification task. The misalignment in numerical simulation is estimated by manipulating the optical intensity distributions in the fourth focus plane in the 4f system.
GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data.
A Concurrent Switching Model for Traffic Congestion Control
We introduce a new conservation-based approach for traffic coordination modeling and control in a network of interconnected roads (NOIR) with switching movement phase rotations at every NOIR junction.
A trained humanoid robot can perform human-like crossmodal social attention and conflict resolution
Our saliency prediction model was trained to detect social cues, predict audio-visual saliency, and attend selectively for the robot study.
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective
Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.
Apache Submarine: A Unified Machine Learning Platform Made Simple
As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity.
Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.
Conservation-Based Modeling and Boundary Control of Congestion with an Application to Traffic Management in Center City Philadelphia
By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR with consideration of the inner traffic dynamics as a stochastic process.
What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the environment.
Hybrid Low-order and Higher-order Graph Convolutional Networks
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved.
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment.
Expectation Learning for Adaptive Crossmodal Stimuli Association
The human brain is able to learn, generalize, and predict crossmodal stimuli.
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Then we extend the model family to a variety of bayesian online models with increasing feature embedding capabilities, such as Sparse-MLP, FM-MLP and FFM-MLP.
