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Found 24 papers, 7 papers with code
CogBERT: Cognition-Guided Pre-trained Language Models
To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.
Neural Natural Logic Inference for Interpretable Question Answering
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.
Autoregressive Modeling with Lookahead Attention
To predict the next token, autoregressive models ordinarily examine the past.
A Measure-Theoretic Characterization of Tight Language Models
Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
CSQ stabilizes the bit-level mixed-precision training process with a bi-level gradual continuous sparsification on both the bit values of the quantized weights and the bit selection in determining the quantization precision of each layer.
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner.
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer.
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning
We propose to select layers based on the variability of their hidden states given a task-specific corpus.
Uncertainty Guided Depth Fusion for Spike Camera
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?
Given a task, human learns from easy to hard, whereas the model learns randomly.
A Graph Enhanced BERT Model for Event Prediction
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression
To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps.
Learning Event Graph Knowledge for Abductive Reasoning
Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning
ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder
Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP).
Training Deep Neural Networks Using Posit Number System
With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations.
The Curious Case of Neural Text Degeneration
Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators.
An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)
The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces.
A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.
A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed.
