Advanced Deep Learning

LARGE PRE-TRAINED GENERATION MODELS, SUCH AS OPENAI X, CAN GENERATE SYNTAX- AND FUNCTION-CORRECT , MAKING THE CODING OF PROGRAMMERS MORE PRODUCTIVE AND OUR PURSUIT OF ARTIFICIAL GENERAL INTELLIGENCE CLOSER.

TO SHOWCASE THE POWER OF THE PROPOSED APPROACH, WE GENERATE DATASETS WITH RICH DENSE PIXEL-WISE LABELS FOR A WIDE RANGE OF DOWNSTREAM TASKS, INCLUDING SEMANTIC SEGMENTATION, INSTANCE SEGMENTATION, AND DEPTH ESTIMATION.

WE PROPOSE A NOVEL FRAMEWORK CIPHERCHAT TO SYSTEMATICALLY EXAMINE THE GENERALIZABILITY OF SAFETY ALIGNMENT TO NON-NATURAL LANGUAGES -- CIPHERS.

WE FIRST PRESENT TOOLBENCH, AN INSTRUCTION-TUNING DATASET FOR TOOL USE, WHICH IS CREATED AUTOMATICALLY USING CHATGPT.

MOREOVER, OUR MODEL EVEN OUTPERFORMS THE LARGEST CLOSED LLMS, ANTHROPIC'S CLAUDE AND GOOGLE'S BARD, ON HUMANEVAL AND HUMANEVAL+.

HOWEVER, RLHF IS A COMPLEX AND OFTEN UNSTABLE PROCEDURE, FIRST FITTING A REWARD MODEL THAT REFLECTS THE HUMAN PREFERENCES, AND THEN FINE-TUNING THE LARGE UNSUPERVISED LM USING REINFORCEMENT LEARNING TO MAXIMIZE THIS ESTIMATED REWARD WITHOUT DRIFTING TOO FAR FROM THE ORIGINAL MODEL.

WE PRESENT A NEAR REAL-TIME METHOD FOR 6-DOF TRACKING OF AN UNKNOWN OBJECT FROM A MONOCULAR RGBD VIDEO SEQUENCE, WHILE SIMULTANEOUSLY PERFORMING NEURAL 3D RECONSTRUCTION OF THE OBJECT.

WE DEMONSTRATE FAN ON A REAL-WORLD ROBOTIC SYSTEM (A MICRO AERIAL VEHICLE) AND REPORT ITS ABILITY TO SEAMLESSLY FOLLOW THE OBJECTS OF INTEREST IN A REAL-TIME CONTROL LOOP.

IN THIS WORK, WE INTRODUCE AUDIOSEP, A FOUNDATION MODEL FOR OPEN-DOMAIN AUDIO SOURCE SEPARATION WITH NATURAL LANGUAGE QUERIES.

FROM THE APPLICATION POINT OF VIEW, A USER NEEDS TO GENERATE THE MASKS FOR THE OBJECTS THEY WOULD LIKE TO REMOVE WHICH CAN BE TIME-CONSUMING AND PRONE TO ERRORS.

WE PRESENT A FAMILY OF FINE-TUNED AND MERGED LARGE LANGUAGE MODELS (LLMS) THAT ACHIEVES THE STRONGEST PERFORMANCE AND CURRENTLY STANDS AT FIRST PLACE IN HUGGINGFACE'S OPEN LLM LEADERBOARD AS OF THE RELEASE DATE OF THIS WORK.

TO ADDRESS THIS ISSUE, WE PROPOSE A GENERIC AND LIGHTWEIGHT CONTROLLABLE KNOWLEDGE RE-INJECTION MODULE, WHICH UTILIZES THE SOPHISTICATED REASONING ABILITY OF LLMS TO CONTROL THE VPG TO CONDITIONALLY EXTRACT INSTRUCTION-SPECIFIC VISUAL INFORMATION AND RE-INJECT IT INTO THE LLM.

THE TOOL PERFORMS SIGNIFICANTLY BETTER THAN OTHER OPEN-SOURCE SOLUTIONS IN THIS EVALUATION AND IN EXTERNAL BENCHMARKS.

3D VISION-LANGUAGE GROUNDING (3D-VL) IS AN EMERGING FIELD THAT AIMS TO CONNECT THE 3D PHYSICAL WORLD WITH NATURAL LANGUAGE, WHICH IS CRUCIAL FOR ACHIEVING EMBODIED INTELLIGENCE.

WE PRESENT THE CONTENT DEFORMATION FIELD F AS A NEW TYPE OF VIDEO REPRESENTATION, WHICH CONSISTS OF A CANONICAL CONTENT FIELD AGGREGATING THE STATIC CONTENTS IN THE ENTIRE VIDEO AND A TEMPORAL DEFORMATION FIELD RECORDING THE TRANSFORMATIONS FROM THE CANONICAL IMAGE (I. E., RENDERED FROM THE CANONICAL CONTENT FIELD) TO EACH INDIVIDUAL FRAME ALONG THE TIME AXIS. GIVEN A TARGET VIDEO, THESE TWO FIELDS ARE JOINTLY OPTIMIZED TO RECONSTRUCT IT THROUGH A CAREFULLY TAILORED RENDERING PIPELINE. WE ADVISEDLY INTRODUCE SOME REGULARIZATIONS INTO THE OPTIMIZATION PROCESS, URGING THE CANONICAL CONTENT FIELD TO INHERIT SEMANTICS (E. G., THE OBJECT SHAPE) FROM THE VIDEO. WITH SUCH A DESIGN, F NATURALLY SUPPORTS LIFTING IMAGE ALGORITHMS FOR VIDEO PROCESSING, IN THE SENSE THAT ONE CAN APPLY AN IMAGE ALGORITHM TO THE CANONICAL IMAGE AND EFFORTLESSLY PROPAGATE THE OUTCOMES TO THE ENTIRE VIDEO WITH THE AID OF THE TEMPORAL DEFORMATION FIELD. WE EXPERIMENTALLY SHOW THAT F IS ABLE TO LIFT IMAGE-TO-IMAGE TRANSLATION TO VIDEO-TO-VIDEO TRANSLATION AND LIFT KEYPOINT DETECTION TO KEYPOINT TRACKING WITHOUT ANY TRAINING. MORE IMPORTANTLY, THANKS TO OUR LIFTING STRATEGY THAT DEPLOYS THE ALGORITHMS ON ONLY ONE IMAGE, WE ACHIEVE SUPERIOR CROSS-FRAME CONSISTENCY IN PROCESSED VIDEOS COMPARED TO EXISTING VIDEO-TO-VIDEO TRANSLATION APPROACHES, AND EVEN MANAGE TO TRACK NON-RIGID OBJECTS LIKE WATER AND SMOG. PROJECT PAGE CAN BE FOUND AT IMAGE-TO-IMAGE TRANSLATION KEYPOINT DETECTION +1

IN THIS WORK, WE PROPOSE A NOVEL TRAINING MECHANISM TERMED SEGPROMPT THAT USES CATEGORY INFORMATION TO IMPROVE THE MODEL'S CLASS-AGNOSTIC SEGMENTATION ABILITY FOR BOTH KNOWN AND UNKNOWN CATEGORIES.

IN THIS ARTICLE WE SHOW HOW THE PROBLEM OF NEURAL TEXT GENERATION CAN BE CONSTRUCTIVELY REFORMULATED IN TERMS OF TRANSITIONS BETWEEN THE STATES OF A FINITE-STATE MACHINE.

TO THIS END, WE INTRODUCE A NOVEL TOKEN MIXING OPERATOR, REPMIXER, A BUILDING BLOCK OF FASTVIT, THAT USES STRUCTURAL REPARAMETERIZATION TO LOWER THE MEMORY ACCESS COST BY REMOVING SKIP-CONNECTIONS IN THE NETWORK.

BELIEVABLE PROXIES OF HUMAN BEHAVIOR CAN EMPOWER INTERACTIVE APPLICATIONS RANGING FROM IMMERSIVE ENVIRONMENTS TO REHEARSAL SPACES FOR INTERPERSONAL COMMUNICATION TO PROTOTYPING TOOLS.

WE BENCHMARK COMMITPACK AGAINST OTHER NATURAL AND SYNTHETIC INSTRUCTIONS (XP3X, SELF-INSTRUCT, OASST) ON THE 16B PARAMETER STARR MODEL, AND ACHIEVE STATE-OF-THE-ART PERFORMANCE AMONG MODELS NOT TRAINED ON OPENAI OUTPUTS, ON THE HUMANEVAL PYTHON BENCHMARK (46. 2% PASS@1).