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Found 56 papers, 23 papers with code
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks.
Boosting Visual Recognition for Autonomous Driving in Real-world Degradations with Deep Channel Prior
The environmental perception of autonomous vehicles in normal conditions have achieved considerable success in the past decade.
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.
Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.
One-stage Prompt-based Continual Learning
With the QR loss, our approach maintains ~ 50% computational cost reduction during inference as well as outperforms the prior two-stage PCL methods by ~1. 4% on public class-incremental continual learning benchmarks including CIFAR-100, ImageNet-R, and DomainNet.
Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.
All-optical complex field imaging using diffractive processors
Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing.
Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air.
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation.
Information hiding cameras: optical concealment of object information into ordinary images
This information hiding transformation is valid for infinitely many combinations of secret messages, all of which are transformed into ordinary-looking output patterns, achieved all-optically through passive light-matter interactions within the optical processor.
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding.
Improving the Generation Quality of Watermarked Large Language Models via Word Importance Scoring
In this work, we propose to improve the quality of texts generated by a watermarked language model by Watermarking with Importance Scoring (WIS).
GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs).
FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis.
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images.
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different system implementations.
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Offline constrained reinforcement learning (RL) aims to learn a policy that maximizes the expected cumulative reward subject to constraints on expected cumulative cost using an existing dataset.
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Although the efficiency of SNNs can be realized on the In-Memory Computing (IMC) architecture, we show that the energy cost and latency of SNNs scale linearly with the number of timesteps used on IMC hardware.
Do We Really Need a Large Number of Visual Prompts?
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored.
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation.
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs).
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
However, some essential questions exist pertaining to SNNs that are little studied: Do SNNs trained with surrogate gradient learn different representations from traditional Artificial Neural Networks (ANNs)?
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
The channel-wise shifting aligns the center of each channel for removal of outlier asymmetry.
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs).
SEENN: Towards Temporal Spiking Early-Exit Neural Networks
However, we observe that the information capacity in SNNs is affected by the number of timesteps, leading to an accuracy-efficiency tradeoff.
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Though the existing pruning methods can provide extremely high weight sparsity for deep SNNs, the high weight sparsity brings a workload imbalance problem.
Exploring Temporal Information Dynamics in Spiking Neural Networks
After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration.
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes.
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies
Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations.
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions.
All-optical image classification through unknown random diffusers using a single-pixel diffractive network
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
To scale up a pruning technique towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states that dense networks contain smaller subnetworks (i. e., winning tickets) that achieve comparable performance to the dense networks.
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge
Moreover, when applied to the non-stationary linear bandit setting by using a linear kernel, our algorithm is nearly minimax optimal, solving an open problem in the non-stationary linear bandit literature.
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.
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.
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
During its training, random diffusers with a range of correlation lengths were used to improve the diffractive network's generalization performance.
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
However, there is little attention towards additional challenges emerging when federated aggregation is performed in a continual learning system.
Neuromorphic Data Augmentation for Training Spiking Neural Networks
In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance.
Ranked #1 on
Event data classification
on CIFAR10-DVS
(using extra training data)
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
With QDROP, the limit of PTQ is pushed to the 2-bit activation for the first time and the accuracy boost can be up to 51. 49%.
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Then we introduce the temporal efficient training (TET) approach to compensate for the loss of momentum in the gradient descent with SG so that the training process can converge into flatter minima with better generalizability.
Neural Architecture Search for Spiking Neural Networks
Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information.
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Based on the introduced finite difference gradient, we propose a new family of Differentiable Spike (Dspike) functions that can adaptively evolve during training to find the optimal shape and smoothness for gradient estimation.
Ranked #4 on
Event data classification
on CIFAR10-DVS
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
Real World Robustness from Systematic Noise
Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system.
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.
Diversifying Sample Generation for Accurate Data-Free Quantization
Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model.
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity.
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.
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides.
DeepFacePencil: Creating Face Images from Freehand Sketches
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches.
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.
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.
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images.
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.
