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Found 12 papers, 3 papers with code
LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis.
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models
Diffusion models have emerged as preeminent contenders in the realm of generative models.
Investigating the Impact of Quantization on Adversarial Robustness
Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for deployment.
Retraining-free Model Quantization via One-Shot Weight-Coupling Learning
Conversely, mixed-precision quantization (MPQ) is advocated to compress the model effectively by allocating heterogeneous bit-width for layers.
Towards Fair and Comprehensive Comparisons for Image-Based 3D Object Detection
In this work, we build a modular-designed codebase, formulate strong training recipes, design an error diagnosis toolbox, and discuss current methods for image-based 3D object detection.
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc.
RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution
Our experiments on a public Gendered Ambiguous Pronouns (GAP) dataset show that with the supervision learning of the syntactic dependency graph and without fine-tuning the entire BERT, we increased the F1-score of the previous best model (RGCN-with-BERT) from 80. 3% to 82. 5%, compared to the F1-score by single BERT embeddings from 78. 5% to 82. 5%.
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters.
DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space
Specifically, the inherited feature originates from "parent" nodes and is weighted by an inheritance factor.
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.
An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection
We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting.
Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Network
We use a generative model with latent variable to build the relationship between the unobserved confounders and the observed variables(tested variable and the proxy variables).
