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Showing 1 to 9 of 9 for “"Fast inference"”.

  1. All Analog CNN Accelerator with RRAMs for Fast Inference

    … network is more demanding. The need to enable fast and energy efficient circuits for computing deep neural networks is urgent. Most current research works propose dedicated hardware for data to reuse thousands of times. However, while re-using the same hardware to perform the same computation …

    mit Repository record for All Analog CNN Accelerator with RRAMs for Fast Inference (opens in a new tab)

  2. Temporally consistent FastDVDNet: an overlap loss implementation for FastDVDNet

    … objective of this thesis is the improvement of FastDVDNet’s temporal performance within the video denoising problem space. Video denoising refers to the removal of undesired artifacts, or noise, from a given video sequence. Due to the temporal nature of video sequences, flickering or temporal …

    eastern-wash Repository record for Temporally consistent FastDVDNet: an overlap loss implementation for FastDVDNet (opens in a new tab)

  3. Reduced traces and JITing in Church

    … probabilistic programming language, designed for inference. By allowing for easy description and manipulation of distributions, it allows one to describe classical Al models in compact ways, providing a language for very rich expression. However, for inference in Bayes nets, Hidden Markov Models, …

    mit Repository record for Reduced traces and JITing in Church (opens in a new tab)

  4. Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling

    … to manage complex dependencies, 2) achieved fast inference of flow separation and vortices near ring gaps by a physics-informed Generative Adversarial Network, and 3) established a lower bound estimation of oil consumption based on the "healthy system" oil distribution pattern. This thesis …

    mit Repository record for Fast modeling of multi-phase mixture transport in piston/ring/liner system via GAN-augmented progressive modeling (opens in a new tab)

  5. A few-shot learning method for single-object visual anomaly detection

    … We also show that the proposed model boasts fast inference times, which is a plus for industry applications. This project is funded in part by Axiom Plastics Inc., and we have evaluated the proposed method on a proprietary dataset provided by Axiom. The results confirm that the proposed …

    uoit Repository record for A few-shot learning method for single-object visual anomaly detection (opens in a new tab)

  6. Aerial reconstructions via probabilistic data fusion

    … units (GPUs) and parallel programming to allow fast inference. Achieving real time rendering of scenes with hundreds of thousands of geometric primitives and inferring latent appearance, camera pose and geometry in the order of seconds each.

    mit Repository record for Aerial reconstructions via probabilistic data fusion (opens in a new tab)

  7. Co-Designing Efficient Systems and Algorithms for Sparse and Quantized Deep Learning Computing

    … LLM serving. TinyChat boosts edge LLM inference by 3× using activation-aware weight quantization (AWQ). QServe further improves performance with activation and KV cache quantization, enhancing the throughput of NVIDIA TensorRT-LLM by 1.2-2.4× on A100 GPUs. Finally, we introduce HART, an …

    mit Repository record for Co-Designing Efficient Systems and Algorithms for Sparse and Quantized Deep Learning Computing (opens in a new tab)

  8. The Neural Processes Family: Translation Equivariance and Output Dependencies

    … produce well-calibrated predictions, enable fast inference at test time, and have flexible data-handling properties that make them a good candidate for messy real-world datasets and applications. However, this thesis focuses on addressing two shortcomings when applying neural processes to …

    cambridge Repository record for The Neural Processes Family: Translation Equivariance and Output Dependencies (opens in a new tab)

  9. Deep Generative Models and Biological Applications

    … (VAE) framework is an efficient high-dimensional inference method to modeling complicated data manifold in an approximate Bayesian way, i.e., variational inference. </p><p>We first discuss how to design fast stochastic backpropagation algorithm for the VAE based amortized variational inference

    duke Repository record for Deep Generative Models and Biological Applications (opens in a new tab)