temperature sampling,softmax,diversity
Temperature scales logits before softmax. Higher = more random. Lower = more focused. Controls creativity.
653 technical terms and definitions
Temperature scales logits before softmax. Higher = more random. Lower = more focused. Controls creativity.
Adjust logit temperature to control output randomness (low=deterministic high=creative).
Vary temperature during generation.
Temperature sensors provide feedback for thermal control systems.
Monitor junction temperature.
Reduce temperature to sharpen.
Temperature shock applies rapid temperature transitions revealing thermal stress sensitivity.
Temperature testing characterizes functionality across operating temperature range.
Temperature dependence in electromigration accelerates failure exponentially with junction temperature.
Combined temperature humidity and voltage stress.
Investigate THB failures.
Temperature controls randomness; higher = more creative, lower = more deterministic. Top-p filters to the most probable tokens until prob mass p.
Fill templates to generate text.
Template-based prompting uses predefined structures filled with variable content.
Find when actions occur in video.
Find when actions occur in videos.
Segment video into actions.
Attend across video frames.
Encode information in spike timing.
Consistency across time.
Maintain coherence across frames.
Maintain coherence across frames.
Temporal consistency ensures smooth transitions and coherent motion across video frames.
Maintain coherent appearance across video frames.
Temporal context models time-dependent preferences like seasonal interests or daily patterns.
Contrast nearby vs distant frames.
Average predictions over training.
Order events chronologically.
Filter by date/time.
Temporal filtering limits retrieval to documents within date ranges.
Temporal Fusion Transformer combines LSTM encoder-decoder with multi-head attention for interpretable multi-horizon time series forecasting with covariates.
GNNs for dynamic graphs.
Extract time-related medical info.
Temporal point process GNNs model event sequences on graphs through learned intensity functions.
Temporal point processes model event sequences in continuous time by specifying conditional intensity functions governing event occurrence rates.
Temporal random walks respect edge timestamps when sampling paths for representation learning.
Understand time-based relationships.
Reason about time and sequences.
Sample sparse frames from video.
Efficient temporal modeling.
Temporal smoothing in dynamic graphs regularizes learned representations to change gradually over time.
Attach to carrier for processing.
Reversible bonding for processing.
Film pulls inward can cause cracking.
Memory layout of tensors.
Tensor Cores (NVIDIA) accelerate matrix operations. Mixed precision (FP16/BF16 inputs, FP32 accumulate). Key for AI.
Specialized hardware for matrix operations.
Represent chemical tensors efficiently.
Tensor decomposition factorizes weight tensors reducing parameters while maintaining capacity.
Tensor factorization extends matrix methods to higher-order tensors for context-aware recommendations.