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22 Nov

How to Assist Human Agents & Transform Customer Experience with Conversational AI?

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Modern businesses operate under the pressure of high-expectation lifestyles. Customers expect better efficiency and personalization in one click. But honestly, human agents are only human. They cannot be everywhere at the same time, and at times, they need help. That’s when Conversational AI enters the play, it is the game-changer in transforming the customer experience. You may hear about chatbots or virtual assistants, but conversational AI is a few steps ahead of them. It’s no longer about answering simple questions, it’s more about having the interactions that feel and sound more natural, helpful, and seamless. So let’s see how exactly…

22 Nov

Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios

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arXiv:2411.13754v1 Announce Type: new Abstract: Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the immediate recognition and localization of objects and events. Here, we introduce a fully neural Iterative and Parallel Reasoning Mechanism (IPRM) that combines two distinct forms of computation -- iterative and parallel -- to better address complex VQA scenarios. Specifically, IPRM's "iterative" computation facilitates compositional step-by-step reasoning for scenarios wherein individual operations need to be computed, stored, and recalled dynamically (e.g. when computing the query "determine the color of pen to the left of…

22 Nov

VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference

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arXiv:2411.13607v1 Announce Type: new Abstract: Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms struggle to produce accurate monocular 4D poses because of occlusions, partial views, and human-object interactions. They are limited by the viewing angle, pixel density, and sampling rate of the cameras and fail to estimate fast and subtle movements, such as in the musical effect of…

22 Nov

CulturePark: Boosting Cross-cultural Understanding in Large Language Models

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[Submitted on 24 May 2024 (v1), last revised 21 Nov 2024 (this version, v3)] View a PDF of the paper titled CulturePark: Boosting Cross-cultural Understanding in Large Language Models, by Cheng Li and 5 other authors View PDF HTML (experimental) Abstract:Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchmarks are constructed either by extracting subsets of existing datasets or by aggregating from platforms such as Wikipedia and social media. However, these approaches are highly dependent on real-world data and human annotations, making them…

22 Nov

Truthfulness of Calibration Measures

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[Submitted on 19 Jul 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Truthfulness of Calibration Measures, by Nika Haghtalab and 3 other authors View PDF HTML (experimental) Abstract:We initiate the study of the truthfulness of calibration measures in sequential prediction. A calibration measure is said to be truthful if the forecaster (approximately) minimizes the expected penalty by predicting the conditional expectation of the next outcome, given the prior distribution of outcomes. Truthfulness is an important property of calibration measures, ensuring that the forecaster is not incentivized to exploit the system with…

22 Nov

Msmsfnet: a multi-stream and multi-scale fusion net for edge detection

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[Submitted on 7 Apr 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Msmsfnet: a multi-stream and multi-scale fusion net for edge detection, by Chenguang Liu and 7 other authors View PDF HTML (experimental) Abstract:Edge detection is a long-standing problem in computer vision. Recent deep learning based algorithms achieve state-of-the-art performance in publicly available datasets. Despite their efficiency, their performance, however, relies heavily on the pre-trained weights of the backbone network on the ImageNet dataset. This significantly limits the design space of deep learning based edge detectors. Whenever we want to devise…

22 Nov

Interactive and Expressive Code-Augmented Planning with Large Language Models

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22 Nov

Federated Continual Learning for Edge-AI: A Comprehensive Survey

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arXiv:2411.13740v1 Announce Type: new Abstract: Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-the-art research and present the first…

22 Nov

Deep Feature Response Discriminative Calibration

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arXiv:2411.13582v1 Announce Type: new Abstract: Deep neural networks (DNNs) have numerous applications across various domains. Several optimization techniques, such as ResNet and SENet, have been proposed to improve model accuracy. These techniques improve the model performance by adjusting or calibrating feature responses according to a uniform standard. However, they lack the discriminative calibration for different features, thereby introducing limitations in the model output. Therefore, we propose a method that discriminatively calibrates feature responses. The preliminary experimental results indicate that the neural feature response follows a Gaussian distribution. Consequently, we compute confidence values by employing the Gaussian probability density function, and…

22 Nov

InstCache: A Predictive Cache for LLM Serving

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arXiv:2411.13820v1 Announce Type: new Abstract: Large language models are revolutionizing every aspect of human life. However, the unprecedented power comes at the cost of significant computing intensity, suggesting long latency and large energy footprint. Key-Value Cache and Semantic Cache have been proposed as a solution to the above problem, but both suffer from limited scalability due to significant memory cost for each token or instruction embeddings. Motivated by the observations that most instructions are short, repetitive and predictable by LLMs, we propose to predict user-instructions by an instruction-aligned LLM and store them in a predictive cache, so-called InstCache. We introduce…