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Found 13 papers, 1 papers with code
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding
To resolve this "discord" between discrete and continuous representations, we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that decodes discrete motion tokens into continuous motion through rectified flow.
Ranked #4 on
Motion Synthesis
on HumanML3D
SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism
In this paper, we propose a novel compression methodology that dynamically determines the rank of each layer using a soft thresholding mechanism, which clips the singular values with a small magnitude in a differentiable form.
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques.
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction, classification, and on-chip few-shot learning (FSL) through gradient-free learning techniques in a 40 nm CMOS process.
An Analog and Digital Hybrid Attention Accelerator for Transformers with Charge-based In-memory Computing
The attention mechanism is a key computing kernel of Transformers, calculating pairwise correlations across the entire input sequence.
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements.
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date.
Ranked #2 on
Classification
on MHIST
(using extra training data)
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation
The combined in-memory pruning and on-chip recompute of the relevant attention scores enables SPRINT to transform quadratic complexity to a merely linear one.
Accelerating Attention through Gradient-Based Learned Runtime Pruning
To best utilize this mathematical innovation, we devise a bit-serial architecture, dubbed LeOPArd, for transformer language models with bit-level early termination microarchitectural mechanism.
RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss
Semi-supervised learning (SSL) has played an important role in leveraging unlabeled data when labeled data is limited.
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification
Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation.
ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation
We obtain promising results (4. 21% error rate on CIFAR-10 with 4000 labels, 22. 32% error rate on CIFAR-100 with 10000 labels, and 2. 19% error rate on SVHN with 1000 labels) when the amount of labeled data is sufficient to learn semantics-oriented similarity representation.
Applying Tensor Decomposition to image for Robustness against Adversarial Attack
However, it turns out a deep learning based model is highly vulnerable to some small perturbation called an adversarial attack.
