Datasets:

takara-ai
/

daily-papers-popularity

date stringdate 2023-05-04 00:00:00 2025-08-27 00:00:00	arxiv_id stringlengths 10 10	votes int32 0 110M	title stringlengths 8 206	abstract stringlengths 165 1.92k	url stringlengths 40 40
2023-05-04	2305.03048	9	Personalize Segment Anything Model with One Shot	Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your...	https://huggingface.co/papers/2305.03048
2023-05-04	2305.02483	3	ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization	Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we propose a tri-agent generation pipeline comprising a generator, an instructor, and an editor to enhance output personalization. The generator p...	https://huggingface.co/papers/2305.02483
2023-05-04	2305.02463	3	Shap-E: Generating Conditional 3D Implicit Functions	We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages:...	https://huggingface.co/papers/2305.02463
2023-05-04	2305.03047	1	Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision	Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependen...	https://huggingface.co/papers/2305.03047
2023-05-05	2305.02549	6	FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction	The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additiona...	https://huggingface.co/papers/2305.02549
2023-05-05	2305.03043	5	Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization	There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Method...	https://huggingface.co/papers/2305.03043
2023-05-05	2305.03049	3	NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds	This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud re...	https://huggingface.co/papers/2305.03049
2023-05-05	2305.02665	3	Learning Language-Specific Layers for Multilingual Machine Translation	Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g., avoiding losing gender and formality information when translating through English). On th...	https://huggingface.co/papers/2305.02665
2023-05-05	2305.02499	3	AutoML-GPT: Automatic Machine Learning with GPT	AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like...	https://huggingface.co/papers/2305.02499
2023-05-05	2305.02783	2	Automated Code generation for Information Technology Tasks in YAML through Large Language Models	The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, have received far less attention, despite involving many active developers and being an essential...	https://huggingface.co/papers/2305.02783
2023-05-05	2305.03052	1	Tracking through Containers and Occluders in the Wild	Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequ...	https://huggingface.co/papers/2305.03052
2023-05-05	2305.03040	1	TUVF: Learning Generalizable Texture UV Radiance Fields	Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generat...	https://huggingface.co/papers/2305.03040
2023-05-05	2305.03027	1	NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads	We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps. To this end, we propose a new multi-view capture setup composed of 16 calibrated machine vision cameras that record time-synchroniz...	https://huggingface.co/papers/2305.03027
2023-05-05	2305.02968	1	Masked Trajectory Models for Prediction, Representation, and Control	We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruct the trajectory conditioned on random subsets of the same trajectory. By training with a highly randomized masking pattern, MTM learns vers...	https://huggingface.co/papers/2305.02968
2023-05-05	2305.02790	1	BranchNorm: Robustly Scaling Extremely Deep Transformers	Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNorm (Wang et al., 2022) attempts to constrain the model update to a constant value. Although applying such a constraint can benefit the early...	https://huggingface.co/papers/2305.02790
2023-05-05	2305.02678	1	Real-Time Neural Appearance Models	We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which pro...	https://huggingface.co/papers/2305.02678
2023-05-05	2305.02440	1	Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs	Large language models (LLMs) power many state-of-the-art systems in natural language processing. However, these models are extremely computationally expensive, even at inference time, raising the natural question: when is the extra cost of deploying a larger model worth the anticipated boost in capabilities? Better und...	https://huggingface.co/papers/2305.02440
2023-05-05	2305.02412	1	Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents	Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints ...	https://huggingface.co/papers/2305.02412
2023-05-07	2305.03111	10	Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs	Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema w...	https://huggingface.co/papers/2305.03111
2023-05-07	2305.03726	6	Otter: A Multi-Modal Model with In-Context Instruction Tuning	Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-wo...	https://huggingface.co/papers/2305.03726
2023-05-07	2305.03695	4	Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements	Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects on the correctness of LM outputs, and introduce Vera, a general-purpose model that estimates the plausibility of declarat...	https://huggingface.co/papers/2305.03695
2023-05-07	2305.03210	1	AttentionViz: A Global View of Transformer Attention	Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elemen...	https://huggingface.co/papers/2305.03210
2023-05-07	2305.03509	1	Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion	Diffusion-based generative models' impressive ability to create convincing images has captured global attention. However, their complex internal structures and operations often make them difficult for non-experts to understand. We present Diffusion Explainer, the first interactive visualization tool that explains how S...	https://huggingface.co/papers/2305.03509
2023-05-07	2305.03514	1	Can Large Language Models Transform Computational Social Science?	Large Language Models (LLMs) like ChatGPT are capable of successfully performing many language processing tasks zero-shot (without the need for training data). If this capacity also applies to the coding of social phenomena like persuasiveness and political ideology, then LLMs could effectively transform Computational ...	https://huggingface.co/papers/2305.03514
2023-05-07	2305.03719	0	Governance of the AI, by the AI, and for the AI	Over the past half century, there have been several false dawns during which the "arrival" of world-changing artificial intelligence (AI) has been heralded. Tempting fate, the authors believe the age of AI has, indeed, finally arrived. Powerful image generators, such as DALL-E2 and Midjourney have suddenly allowed anyo...	https://huggingface.co/papers/2305.03719
2023-05-08	2305.04745	3	Controllable Light Diffusion for Portraits	We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination. Inspired by professional photographers' diffusers and scrims, our method softens lighting given only a single portrait photo. Previous portrait reli...	https://huggingface.co/papers/2305.04745
2023-05-08	2305.04461	2	Locally Attentional SDF Diffusion for Controllable 3D Shape Generation	Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control the local geometry of generated shapes. To address these challenges, we propose a diffusion-based 3D generation framework -- locally at...	https://huggingface.co/papers/2305.04461
2023-05-08	2305.04160	2	X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages	Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual language models. We attribute this to the use of more advanced LLMs compared with previous multimodal models. Unfortunately, the model archit...	https://huggingface.co/papers/2305.04160
2023-05-08	2305.03689	2	COLA: How to adapt vision-language models to Compose Objects Localized with Attributes?	Compositional reasoning is a hallmark of human visual intelligence; yet despite the size of large vision-language models, they struggle to represent simple compositions by combining objects with their attributes. To measure this lack of compositional capability, we design Cola, a text-to-image retrieval benchmark to Co...	https://huggingface.co/papers/2305.03689
2023-05-08	2305.04391	1	A Variational Perspective on Solving Inverse Problems with Diffusion Models	Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task. Most inverse tasks can be formulated as inferring a posterior distribution o...	https://huggingface.co/papers/2305.04391
2023-05-08	2305.03713	1	Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos	Modern generators render talking-head videos with impressive levels of photorealism, ushering in new user experiences such as videoconferencing under constrained bandwidth budgets. Their safe adoption, however, requires a mechanism to verify if the rendered video is trustworthy. For instance, for videoconferencing we m...	https://huggingface.co/papers/2305.03713
2023-05-08	2305.03668	1	A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding	Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept in existing datasets: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data left ...	https://huggingface.co/papers/2305.03668
2023-05-08	2305.03286	1	Composite Motion Learning with Task Control	We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneousl...	https://huggingface.co/papers/2305.03286
2023-05-09	2305.05176	6	FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance	There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In partic...	https://huggingface.co/papers/2305.05176
2023-05-09	2305.05644	5	Towards Building the Federated GPT: Federated Instruction Tuning	While ``instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amounts of diverse and high-quality instruction data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data, especially when i...	https://huggingface.co/papers/2305.05644
2023-05-09	2305.05662	4	InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language	We present an interactive visual framework named InternChat, or iChat for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, with non-verbal instructions like pointing movements that enable users to directly manipulate images or videos on the screen. Pointing (inclu...	https://huggingface.co/papers/2305.05662
2023-05-09	2305.04091	3	Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models	Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and...	https://huggingface.co/papers/2305.04091
2023-05-09	2305.05189	2	SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models	Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resultin...	https://huggingface.co/papers/2305.05189
2023-05-09	2305.03937	2	Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization	Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-...	https://huggingface.co/papers/2305.03937
2023-05-09	2305.04790	1	MultiModal-GPT: A Vision and Language Model for Dialogue with Humans	We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-e...	https://huggingface.co/papers/2305.04790
2023-05-09	2305.04789	1	AvatarReX: Real-time Expressive Full-body Avatars	We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face together, but also supports real-time animation and rendering. To this end, we propose a compositional avatar representation, where the bod...	https://huggingface.co/papers/2305.04789
2023-05-09	2305.04388	1	Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting	Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM's process for solving a task. However, we find that CoT explana...	https://huggingface.co/papers/2305.04388
2023-05-09	2305.04268	1	Multi-Space Neural Radiance Fields	Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel...	https://huggingface.co/papers/2305.04268
2023-05-09	2305.04241	1	Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens	Transformer models are foundational to natural language processing (NLP) and computer vision. Despite various recent works devoted to reducing the quadratic cost of such models (as a function of the sequence length n), dealing with ultra long sequences efficiently (e.g., with more than 16K tokens) remains challenging. ...	https://huggingface.co/papers/2305.04241
2023-05-09	2305.03981	1	Pre-training Language Model as a Multi-perspective Course Learner	ELECTRA, the generator-discriminator pre-training framework, has achieved impressive semantic construction capability among various downstream tasks. Despite the convincing performance, ELECTRA still faces the challenges of monotonous training and deficient interaction. Generator with only masked language modeling (MLM...	https://huggingface.co/papers/2305.03981
2023-05-10	2305.05065	7	Recommender Systems with Generative Retrieval	Modern recommender systems leverage large-scale retrieval models consisting of two stages: training a dual-encoder model to embed queries and candidates in the same space, followed by an Approximate Nearest Neighbor (ANN) search to select top candidates given a query's embedding. In this paper, we propose a new single-...	https://huggingface.co/papers/2305.05065
2023-05-10	2304.09355	5	To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review	Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and opti...	https://huggingface.co/papers/2304.09355
2023-05-10	2305.05862	4	Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks	The most recent large language models such as ChatGPT and GPT-4 have garnered significant attention, as they are capable of generating high-quality responses to human input. Despite the extensive testing of ChatGPT and GPT-4 on generic text corpora, showcasing their impressive capabilities, a study focusing on financia...	https://huggingface.co/papers/2305.05862
2023-05-10	2305.05591	3	AudioSlots: A slot-centric generative model for audio separation	In a range of recent works, object-centric architectures have been shown to be suitable for unsupervised scene decomposition in the vision domain. Inspired by these methods we present AudioSlots, a slot-centric generative model for blind source separation in the audio domain. AudioSlots is built using permutation-equiv...	https://huggingface.co/papers/2305.05591
2023-05-10	2305.06077	2	Relightify: Relightable 3D Faces from a Single Image via Diffusion Models	Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input. Motivated by this, in this paper, we present...	https://huggingface.co/papers/2305.06077
2023-05-10	2305.05845	2	Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models	The proliferation of video content demands efficient and flexible neural network based approaches for generating new video content. In this paper, we propose a novel approach that combines zero-shot text-to-video generation with ControlNet to improve the output of these models. Our method takes multiple sketched frames...	https://huggingface.co/papers/2305.05845
2023-05-10	2305.05658	2	TidyBot: Personalized Robot Assistance with Large Language Models	For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining...	https://huggingface.co/papers/2305.05658
2023-05-10	2305.05364	2	Large Language Model Programs	In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoni...	https://huggingface.co/papers/2305.05364
2023-05-10	2305.04966	2	NerfAcc: Efficient Sampling Accelerates NeRFs	Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative sampling approaches to help accelerate their methods, however, they are often not the focus of the work. In this paper, we investigate and c...	https://huggingface.co/papers/2305.04966
2023-05-10	2305.05383	2	Code Execution with Pre-trained Language Models	Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can un...	https://huggingface.co/papers/2305.05383
2023-05-10	2305.05432	1	WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset	Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data underused. To study multimodal webpage un...	https://huggingface.co/papers/2305.05432
2023-05-11	2305.06161	31	StarCoder: may the source be with you!	The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. Sta...	https://huggingface.co/papers/2305.06161
2023-05-11	2305.06355	3	VideoChat: Chat-Centric Video Understanding	In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal rela...	https://huggingface.co/papers/2305.06355
2023-05-11	2305.06131	2	Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era	Generative AI (AIGC, a.k.a. AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC. Due to the development in text-to-image as well 3D modeling technologies (like N...	https://huggingface.co/papers/2305.06131
2023-05-11	2305.06356	1	HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion	Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion ...	https://huggingface.co/papers/2305.06356
2023-05-11	2305.06351	1	Reconstructing Animatable Categories from Videos	Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid catego...	https://huggingface.co/papers/2305.06351
2023-05-11	2305.06324	1	Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception	We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combin...	https://huggingface.co/papers/2305.06324
2023-05-11	2305.05973	1	Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models	We propose a novel approach for developing privacy-preserving large-scale recommender systems using differentially private (DP) large language models (LLMs) which overcomes certain challenges and limitations in DP training these complex systems. Our method is particularly well suited for the emerging area of LLM-based ...	https://huggingface.co/papers/2305.05973
2023-05-11	2305.05706	1	DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects	To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better a...	https://huggingface.co/papers/2305.05706
2023-05-11	2305.06218	1	Multi-Task End-to-End Training Improves Conversational Recommendation	In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue. While previous works in this area adopt complex multi-component approaches where the dia...	https://huggingface.co/papers/2305.06218
2023-05-12	2305.06908	6	CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model	Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. I...	https://huggingface.co/papers/2305.06908
2023-05-12	2305.07011	5	Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers	We present Region-aware Open-vocabulary Vision Transformers (RO-ViT) - a contrastive image-text pretraining recipe to bridge the gap between image-level pretraining and open-vocabulary object detection. At the pretraining phase, we propose to randomly crop and resize regions of positional embeddings instead of using th...	https://huggingface.co/papers/2305.07011
2023-05-12	2305.06500	5	InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning	General-purpose language models that can solve various language-domain tasks have emerged driven by the pre-training and instruction-tuning pipeline. However, building general-purpose vision-language models is challenging due to the increased task discrepancy introduced by the additional visual input. Although vision-l...	https://huggingface.co/papers/2305.06500
2023-05-12	2305.07027	4	EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention	Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named EfficientViT. We find tha...	https://huggingface.co/papers/2305.07027
2023-05-12	2305.07015	4	Exploiting Diffusion Prior for Real-World Image Super-Resolution	We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the gen...	https://huggingface.co/papers/2305.07015
2023-05-12	2305.07017	3	An Inverse Scaling Law for CLIP Training	CLIP, one of the pioneering foundation models that connect images and text, has enabled many recent breakthroughs in computer vision. However, its associated training cost is prohibitively high, imposing a significant barrier to its widespread exploration. In this paper, we present a surprising finding that there exist...	https://huggingface.co/papers/2305.07017
2023-05-12	2305.06575	2	Chain-of-Dictionary Prompting Elicits Translation in Large Language Models	Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fact that the amount of training data is gigantic, they still struggle with translating rare words, particularly for low-resource languages. E...	https://huggingface.co/papers/2305.06575
2023-05-12	2305.07021	1	Simple Token-Level Confidence Improves Caption Correctness	The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the correctness of fine-grained details, leading to errors in outputs such as hallucinating objects in generated captions or poor compositional rea...	https://huggingface.co/papers/2305.07021
2023-05-12	2305.07004	1	Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting	Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Speci...	https://huggingface.co/papers/2305.07004
2023-05-12	2305.06594	1	V2Meow: Meowing to the Visual Beat via Music Generation	Generating high quality music that complements the visual content of a video is a challenging task. Most existing visual conditioned music generation systems generate symbolic music data, such as MIDI files, instead of raw audio waveform. Given the limited availability of symbolic music data, such methods can only gene...	https://huggingface.co/papers/2305.06594
2023-05-12	2305.06555	1	Domain Incremental Lifelong Learning in an Open World	Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL models. However, it is non-trivial to extend previous approaches to domain incremental LL scenarios since they either require access to task id...	https://huggingface.co/papers/2305.06555
2023-05-12	2305.06474	1	Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction	Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative...	https://huggingface.co/papers/2305.06474
2023-05-12	2305.06456	1	Perpetual Humanoid Control for Real-time Simulated Avatars	We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any extern...	https://huggingface.co/papers/2305.06456
2023-05-12	2305.06424	1	Bot or Human? Detecting ChatGPT Imposters with A Single Question	Large language models like ChatGPT have recently demonstrated impressive capabilities in natural language understanding and generation, enabling various applications including translation, essay writing, and chit-chatting. However, there is a concern that they can be misused for malicious purposes, such as fraud or den...	https://huggingface.co/papers/2305.06424
2023-05-12	2305.06404	1	LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM	Text embeddings are useful features for several NLP applications, such as sentence similarity, text clustering, and semantic search. In this paper, we present a Low-rank Adaptation with a Contrastive objective on top of 8-bit Siamese-BLOOM, a multilingual large language model optimized to produce semantically meaningfu...	https://huggingface.co/papers/2305.06404
2023-05-14	2305.07185	9	MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers	Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books. We proposed Megabyte, a multi-scale decoder architecture that enables end-to-end differentiable modeling of sequences of over one million bytes. Megabyte se...	https://huggingface.co/papers/2305.07185
2023-05-14	2305.07243	5	Better speech synthesis through scaling	In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodolo...	https://huggingface.co/papers/2305.07243
2023-05-14	2305.07490	1	ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4	In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's ...	https://huggingface.co/papers/2305.07490
2023-05-15	2305.08379	3	TESS: Text-to-Text Self-Conditioned Simplex Diffusion	Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work...	https://huggingface.co/papers/2305.08379
2023-05-15	2305.07447	3	Universal Source Separation with Weakly Labelled Data	Universal source separation (USS) is a fundamental research task for computational auditory scene analysis, which aims to separate mono recordings into individual source tracks. There are three potential challenges awaiting the solution to the audio source separation task. First, previous audio source separation system...	https://huggingface.co/papers/2305.07447
2023-05-15	2305.08850	1	Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts	The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have garnered significant attention. However, previous works are limited to editing con...	https://huggingface.co/papers/2305.08850
2023-05-15	2305.07615	1	What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization	Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work has added a calibration step, which exposes a model to its own ranked outputs to improve relevance or, in a separate line of...	https://huggingface.co/papers/2305.07615
2023-05-15	2305.07558	1	Measuring Progress in Fine-grained Vision-and-Language Understanding	While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained models lack "fine-grained" understanding, such as the ability to recognise relationships, verbs, and numbers in images. This has resulted in ...	https://huggingface.co/papers/2305.07558
2023-05-15	2305.07514	1	BlendFields: Few-Shot Example-Driven Facial Modeling	Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely...	https://huggingface.co/papers/2305.07514
2023-05-15	2305.07378	1	Surfacing Biases in Large Language Models using Contrastive Input Decoding	Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text ...	https://huggingface.co/papers/2305.07378
2023-05-15	2305.07214	1	MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition	In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action r...	https://huggingface.co/papers/2305.07214
2023-05-15	2305.07440	1	Optimizing Memory Mapping Using Deep Reinforcement Learning	Resource scheduling and allocation is a critical component of many high impact systems ranging from congestion control to cloud computing. Finding more optimal solutions to these problems often has significant impact on resource and time savings, reducing device wear-and-tear, and even potentially improving carbon emis...	https://huggingface.co/papers/2305.07440
2023-05-15	2305.07153	0	Towards best practices in AGI safety and governance: A survey of expert opinion	A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve or exceed human performance across a wide range of cognitive tasks. In pursuing this goal, they may develop and deploy AI systems that pose...	https://huggingface.co/papers/2305.07153
2023-05-16	2305.07759	36	TinyStories: How Small Can Language Models Be and Still Speak Coherent English?	Language models (LMs) are powerful tools for natural language processing, but they often struggle to produce coherent and fluent text when they are small. Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can rarely generate coherent and consistent English text beyond a few words even after ex...	https://huggingface.co/papers/2305.07759
2023-05-16	2305.09636	13	SoundStorm: Efficient Parallel Audio Generation	We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach ...	https://huggingface.co/papers/2305.09636
2023-05-16	2305.08596	9	DarkBERT: A Language Model for the Dark Side of the Internet	Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. As studies on the Dark Web commonly require textual analysis of the domain, language models specific to the Dark Web may provide valuable insights to researchers. In this work, we int...	https://huggingface.co/papers/2305.08596
2023-05-16	2305.09617	5	Towards Expert-Level Medical Question Answering with Large Language Models	Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLM...	https://huggingface.co/papers/2305.09617
2023-05-16	2305.07922	5	CodeT5+: Open Code Large Language Models for Code Understanding and Generation	Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified enc...	https://huggingface.co/papers/2305.07922
2023-05-16	2305.08848	4	Small Models are Valuable Plug-ins for Large Language Models	Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-C...	https://huggingface.co/papers/2305.08848
2023-05-16	2305.09662	3	Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation	Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their relian...	https://huggingface.co/papers/2305.09662

End of preview. Expand in Data Studio

From the Frontier Research Team at Takara.ai, we present Daily Papers Popularity — a dataset tracking the popularity of Hugging Face Papers with arXiv metadata. It aggregates daily paper entries with votes, IDs, titles, abstracts (backfilled via the HF API), and URLs, enabling analysis of patterns in paper reception and engagement.

Daily Papers Popularity

Columns: date, arxiv_id, votes, title, abstract, url
Format: Parquet

Load

from datasets import load_dataset
ds = load_dataset("takara-ai/daily-papers-popularity")

Visualisations

Reference charts derived from the dataset. Each visual links to static assets hosted alongside the dataset.

Votes vs Title Length

Votes vs Abstract Length

Votes vs Month

Votes vs Day of Month

Distribution: Daily Paper Concentration

Votes vs Daily Paper Concentration

For research inquiries and press, please reach out to research@takara.ai

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