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Found 13 papers, 8 papers with code
A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI
To address these challenges-and mitigate the typical data-transfer bottleneck of classical Von Neumann architectures-we propose a ferroelectric charge-domain compute-in-memory (CiM) array as the foundational processing element for neuro-symbolic AI.
An overview of domain-specific foundation model: key technologies, applications and challenges
The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios.
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection.
Ranked #1 on
Action Classification (zero-shot)
on WiGesture
Adversarial-MidiBERT: Symbolic Music Understanding Model Based on Unbias Pre-training and Mask Fine-tuning
Recently, pre-trained language models have been widely adopted in SMU because the symbolic music shares a huge similarity with natural language, and the pre-trained manner also helps make full use of limited music data.
PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training
Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously.
Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models.
Ranked #2 on
Person Identification
on WiGesture
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly.
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
The proposed approaches are evaluated on three datasets.
Data Augmentation with Atomic Templates for Spoken Language Understanding
The atomic templates produce exemplars for fine-grained constituents of semantic representations.
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data
In the paper, we focus on spoken language understanding from unaligned data whose annotation is a set of act-slot-value triples.
CrowdHuman: A Benchmark for Detecting Human in a Crowd
There are a total of $470K$ human instances from the train and validation subsets, and $~22. 6$ persons per image, with various kinds of occlusions in the dataset.
Ranked #7 on
Pedestrian Detection
on Caltech
(using extra training data)
