Search Results for author:
Found 27 papers, 7 papers with code
Incorporating Casual Analysis into Diversified and Logical Response Generation
Although the Conditional Variational Auto-Encoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Survey on Modeling of Articulated Objects
3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics.
CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness
Our experiments explore the performance of CliqueParcel, including efficiency, faithfulness, and the trade-off between them.
CAGE: Controllable Articulation GEneration
We address the challenge of generating 3D articulated objects in a controllable fashion.
PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects
Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3. 94 (45. 2%) for objects and 26. 79 (84. 5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories.
Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time.
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
Joint Spectrum and Power Allocation for V2X Communications with Imperfect CSI
More specifically, Bernstein approximations are employed to convert the chance constraint into a calculable constraint, and Bisection search method is proposed to obtain the optimal allocation solution with low complexity.
Incorporating Causal Analysis into Diversified and Logical Response Generation
Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
A multi-aspect attentive network is proposed to automatically attend to different aspects in a review and ensure most of the issues are tackled.
Improving Personality Consistency in Conversation by Persona Extending
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions.
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network
In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements.
Movie Genre Classification by Language Augmentation and Shot Sampling
We evaluate our method on MovieNet and Condensed Movies datasets, achieving approximate 6-9% improvement in mean Average Precision (mAP) over the baselines.
ImageSubject: A Large-scale Dataset for Subject Detection
We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them.
Fine-Grained Control of Artistic Styles in Image Generation
The style vectors are fed to the generator and discriminator to achieve fine-grained control.
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling
We propose ToFu, Topologically consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM.
Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
Stochastic Whitening Batch Normalization
BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization.
Target Guided Emotion Aware Chat Machine
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey
With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices.
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning
Tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming.
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.).
Improving Model Training by Periodic Sampling over Weight Distributions
In this paper, we explore techniques centered around periodic sampling of model weights that provide convergence improvements on gradient update methods (vanilla \acs{SGD}, Momentum, Adam) for a variety of vision problems (classification, detection, segmentation).
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
We perform a variety of analysis using the MNIST dataset and validate the approach with a number of DNN models using pre-trained models on the ImageNet dataset.
Effective Building Block Design for Deep Convolutional Neural Networks using Search
We apply this technique to generate models for multiple image datasets and show that these models achieve performance comparable to state-of-the-art (and even surpassing the state-of-the-art in one case).
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information.
