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VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM
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The Art of Scaling Test-Time Compute for Large Language Models
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ThetaEvolve: Test-time Learning on Open ProblemsMicrosoft / Carnegie Mellon University, University of California, University of Washington, University of Wisconsin-Madison
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LatBot: Distilling Universal Latent Actions for Vision-Language-Action Models
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SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling
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Shifting Work Patterns with Generative AI
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LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs
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Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
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Autoregressive Speech Synthesis without Vector Quantization
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LLMs Get Lost In Multi-Turn Conversation
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I-Con: A Unifying Framework for Representation Learning
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AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools
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AI at Work Is Here. Now Comes the Hard Part
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Towards Making the Most of ChatGPT for Machine Translation
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BitNet: Scaling 1-bit Transformers for Large Language Models
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AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
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Phi-2: A Small Language Model with Reasoning Capability
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Textbooks Are All You Need
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Orca: Progressive Learning from Complex Explanation Traces of GPT-4
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ReWOO: Decoupling Reasoning from Observation for Efficient LLM Reasoning
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Sparks of Artificial General Intelligence: Early experiments with GPT-4
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Language Is Not All You Need: Aligning Perception with Language Models
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Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
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Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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LoRA: Low-Rank Adaptation of Large Language Models
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ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
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D2-Net: A Trainable CNN for Joint Description and Detection of Local FeaturesMicrosoft / Chalmers University of Technology, Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, Département d'Informatique de l'ENS, ETH Zurich, Institute of Science Tokyo, National Institute for Research in Digital Science and Technology
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Deep Residual Learning for Image Recognition
