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Found 37 papers, 14 papers with code
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency.
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.
Fair Causal Feature Selection
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers.
RWKV: Reinventing RNNs for the Transformer Era
We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs.
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting.
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs).
A Fully Memristive Spiking Neural Network with Unsupervised Learning
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule.
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
The natural spiking dynamics of the MIF neuron model are fully differentiable, eliminating the need for gradient approximations that are prevalent in the spiking neural network literature.
Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching
The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights.
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication.
Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR
With the application of artificial intelligence in the financial field, quantitative trading is considered to be profitable.
Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model
The net value of the fund is affected by performance and market, and the researchers try to quantify these effects to predict the future net value by establishing different models.
An Improved Reinforcement Learning Model Based on Sentiment Analysis
The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market.
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses.
Ranked #1 on
3D-Aware Image Synthesis
on FFHQ 256 x 256
Passive frustrated nanomagnet reservoir computing
We therefore propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets.
Deep Video Inpainting Detection
This paper studies video inpainting detection, which localizes an inpainted region in a video both spatially and temporally.
Omni-GAN: On the Secrets of cGANs and Beyond
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.
Ranked #7 on
Conditional Image Generation
on ImageNet 128x128
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.
Deepstrip: High-Resolution Boundary Refinement
In this paper, we target refining the boundaries in high resolution images given low resolution masks.
Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition
Recognizing the expressions of partially occluded faces is a challenging computer vision problem.
Facial Expression Recognition
Facial Expression Recognition (FER)
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images.
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Pre-trained language models like BERT have proven to be highly performant.
DeepStrip: High Resolution Boundary Refinement
In this paper, we target refining the boundaries in high resolution images given low resolution masks.
Reservoir Computing with Planar Nanomagnet Arrays
Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.
K-BERT: Enabling Language Representation with Knowledge Graph
For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.
Sionnx: Automatic Unit Test Generator for ONNX Conformance
Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage.
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning
Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots.
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
Pose Transferrable Person Re-Identification
On the other hand, in addition to the conventional discriminator of GAN (i. e., to distinguish between REAL/FAKE samples), we propose a novel guider sub-network which encourages the generated sample (i. e., with novel pose) towards better satisfying the ReID loss (i. e., cross-entropy ReID loss, triplet ReID loss).
Learning Rich Features for Image Manipulation Detection
Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.
Two-Stream Neural Networks for Tampered Face Detection
We propose a two-stream network for face tampering detection.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in information extraction.
Ranked #3 on
Relation Extraction
on NYT-single
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text.
Ranked #5 on
Text Classification
on TREC-6
