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Found 15 papers, 3 papers with code
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond
To better capture the dialogue information, we propose a 2D view of dialogue based on a time-speaker perspective, where the time and speaker streams of dialogue can be obtained as strengthened input.
Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers
Focused on the two aspects, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies for LLM-based prompt optimizers.
PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in the correct direction.
Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT
Based on ELBERT, an innovative method to accelerate text processing on the GPU platform is developed, solving the difficult problem of making the early exit mechanism work more effectively with a large input batch size.
Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?
Precoding design for maximizing weighted sum-rate (WSR) is a fundamental problem for downlink of massive multi-user multiple-input multiple-output (MU-MIMO) systems.
Elbert: Fast Albert with Confidence-Window Based Early Exit
Despite the great success in Natural Language Processing (NLP) area, large pre-trained language models like BERT are not well-suited for resource-constrained or real-time applications owing to the large number of parameters and slow inference speed.
A Liouville theorem for Möbius invariant equations
In this paper we classify M\"{o}bius invariant differential operators of second order in two dimensional Euclidean space, and establish a Liouville type theorem for general M\"{o}bius invariant elliptic equations.
Analysis of PDEs Differential Geometry
PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data
An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform.
Learning-Based Massive Beamforming
Conventional optimization-based iterative resource allocation algorithms often suffer from slow convergence, especially for massive multiple-input-multiple-output (MIMO) beamforming problems.
Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer
Designing hardware accelerators for deep neural networks (DNNs) has been much desired.
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc..
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.
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding
Firstly, we improve the beam search decoding algorithm to save the storage space.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending
This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting.
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
The prediction phase consists of utilizing a linear transport equation, which describes the propagation of COD images in the fluid flow predicted by NSE, to estimate the future motion of the COD images.
