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When this process is performed post-training, the AdaRound algorithm may be used to minimize loss. For 10 points each:
[10h] Name this process that is classified as either symmetric or asymmetric, depending on whether a zero-point is specified. Applying this process to LLMs has dramatically reduced their VRAM (“vee-RAM”) requirements.
ANSWER: quantization [accept binarization; accept more specific answers such as post-training quantization or quantization-aware training; prompt on PTQ or QAT]
[10m] Quantized neural networks often utilize “straight-through estimators” to estimate “surrogate” examples of this function. In a process named for it, the product of this function with a constant is repeatedly subtracted from the objective function.
ANSWER: gradient [accept surrogate gradient or gradient descent; prompt on SGD; prompt on del]
[10e] Quantization works by reducing the precision of this data type, which is defined in IEEE 754. Doubles are a more precise variant of this type, which is the most common type used to represent decimal numbers.
ANSWER: floats [accept floating-point numbers or FP]
<Other Science>
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Conversion

TeamOpponentPart 1Part 2Part 3TotalParts
British ColumbiaMinnesota0101020ME
Cornell AVanderbilt0101020ME
Cornell BIllinois B0101020ME
FloridaIndiana001010E
Georgia TechUC Berkeley A0101020ME
HarvardNorth Carolina A001010E
Iowa StateYale001010E
Johns HopkinsOhio State0101020ME
NYURIT0101020ME
NorthwesternUC Berkeley B001010E
OttawaLSE001010E
Penn StateToronto C0101020ME
RutgersIllinois A0101020ME
TexasArizona State0101020ME
Toronto AColumbia B0101020ME
UCFGeorgia State0101020ME
VirginiaChicago B001010E
Virginia TechStanford001010E
WUSTL BMichigan001010E
Waterloo BNorth Carolina B0101020ME
Winona StateChicago A001010E

Summary

TournamentExact Match?HeardPPBEasy %Medium %Hard %
2025 ACF NationalsYes2115.71100%57%0%