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Found 55 papers, 11 papers with code
CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training
We analyze the final data mixture, elucidating the characteristics of an optimal data mixture.
Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz
In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0. 85-5 GHz using Ranplan Professional.
SMPR: A structure-enhanced multimodal drug-disease prediction model for drug repositioning and cold start
Furthermore, most repositioning models are only used to complete the relationship matrix, and their practicality is poor when dealing with drug cold start problems.
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval
To address the above problem, we propose a novel and general generative retrieval framework, namely Leverag- ing Document-Oriented Contrastive Learning in Generative Retrieval (DOGR), which leverages contrastive learning to improve generative retrieval tasks.
Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting
Repetitive action counting (RAC) aims to estimate the number of class-agnostic action occurrences in a video without exemplars.
DreamFit: Garment-Centric Human Generation via a Lightweight Anything-Dressing Encoder
However, existing methods often face a dilemma: lightweight approaches, such as adapters, are prone to generate inconsistent textures; while finetune-based methods involve high training costs and struggle to maintain the generalization capabilities of pretrained diffusion models, limiting their performance across diverse scenarios.
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework
In this paper, we propose COEF-VQ, a novel cascaded MLLM framework for better video quality understanding on TikTok.
Hymba: A Hybrid-head Architecture for Small Language Models
We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency.
Design and validation of a fuzzy logic controller for multi-section continuum robots
This paper introduces a simple-structured, model-less fuzzy logic controller for the closed-loop control of continuum robots.
A Simple Background Augmentation Method for Object Detection with Diffusion Model
In computer vision, it is well-known that a lack of data diversity will impair model performance.
A deeper look at depth pruning of LLMs
Large Language Models (LLMs) are not only resource-intensive to train but even more costly to deploy in production.
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction
However, this task faces limitations due to the scarcity of high-quality clinical datasets and the complex relationship between symptoms and herbs.
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
Large Language Models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities from few-shot demonstration exemplars.
VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers
To faithfully recover the clothing details, the extracted garment features are fused with the self-attention outputs of the denoising DiT and the ControlNet.
MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation
Besides, to remove the segmentation dependency, MMTryon uses a parsing-free garment encoder and leverages a novel scalable data generation pipeline to convert existing VITON datasets to a form that allows MMTryon to be trained without requiring any explicit segmentation.
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks
Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility.
Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization
Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations.
Finding needles in a haystack: A Black-Box Approach to Invisible Watermark Detection
In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting.
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on
Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person.
Segment Every Out-of-Distribution Object
Semantic segmentation models, while effective for in-distribution categories, face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects.
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning
We study two natural scaling techniques -- weight pruning and simply training a smaller or larger model, which we refer to as dense scaling -- and their effects on two core capabilities of LLMs: (a) recalling facts presented during pre-training and (b) processing information presented in-context during inference.
Face Encryption via Frequency-Restricted Identity-Agnostic Attacks
As for the weak black-box scenario feasibility, we obverse that representations of the average feature in multiple face recognition models are similar, thus we propose to utilize the average feature via the crawled dataset from the Internet as the target to guide the generation, which is also agnostic to identities of unknown face recognition systems; in nature, the low-frequency perturbations are more visually perceptible by the human vision system.
GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning
Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs. On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem.
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients.
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on
In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing.
SphereFed: Hyperspherical Federated Learning
Federated Learning aims at training a global model from multiple decentralized devices (i. e. clients) without exchanging their private local data.
DRESS: Dynamic REal-time Sparse Subnets
The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource constraints.
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks.
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
We demonstrate that our method can handle the SNN conversion with batch normalization layers and effectively preserve the high accuracy even in 32 time steps.
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints.
Dressing in the Wild by Watching Dance Videos
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
TCMPR: TCM Prescription recommendation based on subnetwork term mapping and deep learning
In addition, the comprehensive experiments of TCMPR with different hyper parameters (i. e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.
Fast query-by-example speech search using separable model
Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality.
Data Augmentation with Adversarial Training for Cross-Lingual NLI
Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages.
Cross-Lingual Natural Language Inference
Data Augmentation
+1
Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
Prior works usually assume that SC offers privacy benefits as only intermediate features, instead of private data, are shared from devices to the cloud.
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Moreover, our calibration algorithm can produce SNN with state-of-the-art architecture on the large-scale ImageNet dataset, including MobileNet and RegNet.
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
Based upon this observation, we propose a novel metric called Neural Mean Discrepancy (NMD), which compares neural means of the input examples and training data.
EIC Physics from An All-Silicon Tracking Detector
The proposed electron-ion collider has a rich physics program to study the internal structure of protons and heavy nuclei.
Nuclear Experiment Nuclear Theory
Domain-Specific Sentiment Lexicons Induced from Labeled Documents
Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies.
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
However, the inversion process only utilizes biased feature statistics stored in one model and is from low-dimension to high-dimension.
exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources
We introduce exBERT, a training method to extend BERT pre-trained models from a general domain to a new pre-trained model for a specific domain with a new additive vocabulary under constrained training resources (i. e., constrained computation and data).
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
RTN: Reparameterized Ternary Network
We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks.
A Robust Self-Learning Framework for Cross-Lingual Text Classification
Based on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks.
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks
We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in neural networks.
Full-stack Optimization for Accelerating CNNs with FPGA Validation
A highlight of our full-stack approach which attributes to the achieved high energy efficiency is an efficient Selector-Accumulator (SAC) architecture for implementing the multiplier-accumulator (MAC) operation present in any digital CNN hardware.
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network
To reduce memory footprint and run-time latency, techniques such as neural network pruning and binarization have been explored separately.
What and Where: A Context-based Recommendation System for Object Insertion
In this work, we propose a novel topic consisting of two dual tasks: 1) given a scene, recommend objects to insert, 2) given an object category, retrieve suitable background scenes.
A Helping Hand: Transfer Learning for Deep Sentiment Analysis
Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains.
Ranked #75 on
Sentiment Analysis
on SST-2 Binary classification
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations.
Pose2Seg: Detection Free Human Instance Segmentation
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Ranked #1 on
Pose-Based Human Instance Segmentation
on OCHuman
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems.
