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data/2402.12479.json

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+{"paper_url": "https://huggingface.co/papers/2402.12479", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Mixtures of Experts Unlock Parameter Scaling for Deep RL](https://huggingface.co/papers/2402.08609) (2024)\n* [Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving](https://huggingface.co/papers/2402.05146) (2024)\n* [Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks](https://huggingface.co/papers/2402.06912) (2024)\n* [Always-Sparse Training by Growing Connections with Guided Stochastic Exploration](https://huggingface.co/papers/2401.06898) (2024)\n* [Identifying Policy Gradient Subspaces](https://huggingface.co/papers/2401.06604) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13349.json

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+{"paper_url": "https://huggingface.co/papers/2402.13349", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people](https://huggingface.co/papers/2401.03604) (2024)\n* [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://huggingface.co/papers/2401.00847) (2024)\n* [3D Human Pose Perception from Egocentric Stereo Videos](https://huggingface.co/papers/2401.00889) (2023)\n* [RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos](https://huggingface.co/papers/2401.12592) (2024)\n* [EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI](https://huggingface.co/papers/2312.16170) (2023)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13573.json

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+{"paper_url": "https://huggingface.co/papers/2402.13573", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers](https://huggingface.co/papers/2401.11605) (2024)\n* [CascadedGaze: Efficiency in Global Context Extraction for Image Restoration](https://huggingface.co/papers/2401.15235) (2024)\n* [SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection](https://huggingface.co/papers/2401.15293) (2024)\n* [Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach](https://huggingface.co/papers/2401.05633) (2024)\n* [Cross-view Masked Diffusion Transformers for Person Image Synthesis](https://huggingface.co/papers/2402.01516) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13577.json

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+{"paper_url": "https://huggingface.co/papers/2402.13577", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models](https://huggingface.co/papers/2312.17661) (2023)\n* [Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models](https://huggingface.co/papers/2402.12058) (2024)\n* [Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://huggingface.co/papers/2402.05376) (2024)\n* [Divide and Conquer for Large Language Models Reasoning](https://huggingface.co/papers/2401.05190) (2024)\n* [Puzzle Solving using Reasoning of Large Language Models: A Survey](https://huggingface.co/papers/2402.11291) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13598.json

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+{"paper_url": "https://huggingface.co/papers/2402.13598", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SPAR: Personalized Content-Based Recommendation via Long Engagement Attention](https://huggingface.co/papers/2402.10555) (2024)\n* [User Embedding Model for Personalized Language Prompting](https://huggingface.co/papers/2401.04858) (2024)\n* [RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation](https://huggingface.co/papers/2402.04527) (2024)\n* [Rethinking Large Language Model Architectures for Sequential Recommendations](https://huggingface.co/papers/2402.09543) (2024)\n* [Parameter-Efficient Conversational Recommender System as a Language Processing Task](https://huggingface.co/papers/2401.14194) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13616.json

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+{"paper_url": "https://huggingface.co/papers/2402.13616", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Do deep neural networks utilize the weight space efficiently?](https://huggingface.co/papers/2401.16438) (2024)\n* [ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation](https://huggingface.co/papers/2312.17446) (2023)\n* [Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning](https://huggingface.co/papers/2401.04105) (2024)\n* [Enhanced Few-Shot Class-Incremental Learning via Ensemble Models](https://huggingface.co/papers/2401.07208) (2024)\n* [A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting](https://huggingface.co/papers/2401.05968) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13720.json

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+{"paper_url": "https://huggingface.co/papers/2402.13720", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding](https://huggingface.co/papers/2401.07851) (2024)\n* [BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models](https://huggingface.co/papers/2401.12522) (2024)\n* [Speculative Streaming: Fast LLM Inference without Auxiliary Models](https://huggingface.co/papers/2402.11131) (2024)\n* [GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding](https://huggingface.co/papers/2402.02082) (2024)\n* [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://huggingface.co/papers/2402.02057) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13753.json

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+{"paper_url": "https://huggingface.co/papers/2402.13753", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Extending LLMs' Context Window with 100 Samples](https://huggingface.co/papers/2401.07004) (2024)\n* [E^2-LLM: Efficient and Extreme Length Extension of Large Language Models](https://huggingface.co/papers/2401.06951) (2024)\n* [LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning](https://huggingface.co/papers/2401.01325) (2024)\n* [InfLLM: Unveiling the Intrinsic Capacity of LLMs for Understanding Extremely Long Sequences with Training-Free Memory](https://huggingface.co/papers/2402.04617) (2024)\n* [LongHeads: Multi-Head Attention is Secretly a Long Context Processor](https://huggingface.co/papers/2402.10685) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13763.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2402.13763", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://huggingface.co/papers/2402.06178) (2024)\n* [FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models](https://huggingface.co/papers/2401.15636) (2024)\n* [Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation](https://huggingface.co/papers/2401.01044) (2024)\n* [DITTO: Diffusion Inference-Time T-Optimization for Music Generation](https://huggingface.co/papers/2401.12179) (2024)\n* [Fast Timing-Conditioned Latent Audio Diffusion](https://huggingface.co/papers/2402.04825) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.13929.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2402.13929", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Diffusion Model with Perceptual Loss](https://huggingface.co/papers/2401.00110) (2023)\n* [AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning](https://huggingface.co/papers/2402.00769) (2024)\n* [Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive](https://huggingface.co/papers/2401.08815) (2024)\n* [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) (2024)\n* [Efficient generative adversarial networks using linear additive-attention Transformers](https://huggingface.co/papers/2401.09596) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.14017.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2402.14017", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [DITTO: Diffusion Inference-Time T-Optimization for Music Generation](https://huggingface.co/papers/2401.12179) (2024)\n* [Improving Diffusion-Based Image Synthesis with Context Prediction](https://huggingface.co/papers/2401.02015) (2024)\n* [Sequential Flow Straightening for Generative Modeling](https://huggingface.co/papers/2402.06461) (2024)\n* [Text2Data: Low-Resource Data Generation with Textual Control](https://huggingface.co/papers/2402.10941) (2024)\n* [Score Distillation Sampling with Learned Manifold Corrective](https://huggingface.co/papers/2401.05293) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2402.14020.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2402.14020", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks](https://huggingface.co/papers/2401.17263) (2024)\n* [Query-Based Adversarial Prompt Generation](https://huggingface.co/papers/2402.12329) (2024)\n* [PAL: Proxy-Guided Black-Box Attack on Large Language Models](https://huggingface.co/papers/2402.09674) (2024)\n* [Weak-to-Strong Jailbreaking on Large Language Models](https://huggingface.co/papers/2401.17256) (2024)\n* [Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning](https://huggingface.co/papers/2402.06255) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}