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Found 17 papers, 4 papers with code
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks
Lightweight design of Convolutional Neural Networks (CNNs) requires co-design efforts in the model architectures and compression techniques.
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset
Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed.
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer
Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Simple Cycle Reservoirs are Universal
Reservoir computation models form a subclass of recurrent neural networks with fixed non-trainable input and dynamic coupling weights.
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer
Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition.
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched.
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
CRPTpro significantly outperforms the next best Proto-RL(C) on 11/12 cross-domain downstream tasks with only 54\% wall-clock pre-training time, exhibiting state-of-the-art pre-training performance with greatly improved pre-training efficiency.
Coherent Event Guided Low-Light Video Enhancement
With frame-based cameras, capturing fast-moving scenes without suffering from blur often comes at the cost of low SNR and low contrast.
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification
The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has shown promising performance for graph representation learning.
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks
However, they follow the traditional structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and sensitive to the structure noise described by edges whose two endpoints belong to different categories.
Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition
Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes.
Uncovering the Local Hidden Community Structure in Social Networks
We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods.
Making Adversarial Examples More Transferable and Indistinguishable
Fast gradient sign attack series are popular methods that are used to generate adversarial examples.
